Functional and hyperfunctional siRNA directed against Bcl-2

ABSTRACT

Efficient sequence specific gene silencing is possible through the use of siRNA technology. By selecting particular siRNAs by rationale design, one can maximize the generation of an effective gene silencing reagent, as well as methods for silencing Bcl-2.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of the filing dates of U.S. patentapplication Ser. No. 11/083,784, filed Mar. 18, 2005 entitled,“Functional and Hyperfunctional siRNA Directed Against Bcl-2,” U.S.patent application Ser. No. 10/714,333, filed Nov. 14, 2003 entitled,“Functional and Hyperfunctional siRNA,” U.S. Provisional ApplicationSer. No. 60/426,137, filed Nov. 14, 2002, entitled “CombinatorialPooling Approach for siRNA Induced Gene Silencing and Methods forSelecting siRNA,” and U.S. Provisional Application Ser. No. 60/502,050,filed Sep. 10, 2003, entitled “Methods for Selecting siRNA,” the entiredisclosures of which are hereby incorporated by reference into thepresent disclosure.

SEQUENCE LISTING

The sequence listing for this application has been submitted inaccordance with 37 CFR §1.52(e) and 37 CFR §1.821 on CD-ROM in lieu ofpaper on a disk containing the sequence listing file entitled“DHARMA_(—)0100-US22_CRF.txt” created Oct. 15, 2007, 80 kb. Applicantshereby incorporate by reference the sequence listing provided on CD-ROMin lieu of paper into the instant specification.

FIELD OF INVENTION

The present invention relates to RNA interference (“RNAi”).

BACKGROUND OF THE INVENTION

Relatively recently, researchers observed that double stranded RNA(“dsRNA”) could be used to inhibit protein expression. This ability tosilence a gene has broad potential for treating human diseases, and manyresearchers and commercial entities are currently investing considerableresources in developing therapies based on this technology.

Double stranded RNA induced gene silencing can occur on at least threedifferent levels: (i) transcription inactivation, which refers to RNAguided DNA or histone methylation; (ii) siRNA induced mRNA degradation;and (iii) mRNA induced transcriptional attenuation.

It is generally considered that the major mechanism of RNA inducedsilencing (RNA interference, or RNAi) in mammalian cells is mRNAdegradation. Initial attempts to use RNAi in mammalian cells focused onthe use of long strands of dsRNA. However, these attempts to induce RNAimet with limited success, due in part to the induction of the interferonresponse, which results in a general, as opposed to a target-specific,inhibition of protein synthesis. Thus, long dsRNA is not a viable optionfor RNAi in mammalian systems.

More recently it has been shown that when short (18-30 bp) RNA duplexesare introduced into mammalian cells in culture, sequence-specificinhibition of target mRNA can be realized without inducing an interferonresponse. Certain of these short dsRNAs, referred to as small inhibitoryRNAs (“siRNAs”), can act catalytically at sub-molar concentrations tocleave greater than 95% of the target mRNA in the cell. A description ofthe mechanisms for siRNA activity, as well as some of its applicationsare described in Provost et al., Ribonuclease Activity and RNA Bindingof Recombinant Human Dicer, E.M.B.O. J., 2002 Nov. 1; 21(21): 5864-5874;Tabara et al., The dsRNA Binding Protein RDE-4 Interacts with RDE-1,DCR-1 and a DexH-box Helicase to Direct RNAi in C. elegans, Cell 2002,Jun. 28; 109(7):861-71; Ketting et al., Dicer Functions in RNAInterference and in Synthesis of Small RNA Involved in DevelopmentalTiming in C. elegans; Martinez et al., Single-Stranded Antisense siRNAsGuide Target RNA Cleavage in RNAi, Cell 2002, Sep. 6; 110(5):563;Hutvagner & Zamore, A microRNA in a multiple-turnover RNAi enzymecomplex, Science 2002, 297:2056.

From a mechanistic perspective, introduction of long double stranded RNAinto plants and invertebrate cells is broken down into siRNA by a TypeIII endonuclease known as Dicer. Sharp, RNA interference—2001, GenesDev. 2001, 15:485. Dicer, a ribonuclease-III-like enzyme, processes thedsRNA into 19-23 base pair short interfering RNAs with characteristictwo base 3′ overhangs. Bernstein, Caudy, Hammond, & Hannon, Role for abidentate ribonuclease in the initiation step of RNA interference,Nature 2001, 409:363. The siRNAs are then incorporated into anRNA-induced silencing complex (RISC) where one or more helicases unwindthe siRNA duplex, enabling the complementary antisense strand to guidetarget recognition. Nykanen, Haley, & Zamore, ATP requirements and smallinterfering RNA structure in the RNA interference pathway, Cell 2001,107:309. Upon binding to the appropriate target mRNA, one or moreendonucleases within the RISC cleaves the target to induce silencing.Elbashir, Lendeckel, & Tuschl, RNA interference is mediated by 21-and22-nucleotide RNAs, Genes Dev 2001, 15:188, FIG. 1.

The interference effect can be long lasting and may be detectable aftermany cell divisions. Moreover, RNAi exhibits sequence specificity.Kisielow, M. et al. (2002) Isoform-specific knockdown and expression ofadaptor protein ShcA using small interfering RNA, J. of Biochemistry363: 1-5. Thus, the RNAi machinery can specifically knock down one typeof transcript, while not affecting closely related mRNA. Theseproperties make siRNA a potentially valuable tool for inhibiting geneexpression and studying gene function and drug target validation.Moreover, siRNAs are potentially useful as therapeutic agents against:(1) diseases that are caused by over-expression or misexpression ofgenes; and (2) diseases brought about by expression of genes thatcontain mutations.

Successful siRNA-dependent gene silencing depends on a number offactors. One of the most contentious issues in RNAi is the question ofthe necessity of siRNA design, i.e., considering the sequence of thesiRNA used. Early work in C. elegans and plants circumvented the issueof design by introducing long dsRNA (see, for instance, Fire, A. et al.(1998) Nature 391:806-811). In this primitive organism, long dsRNAmolecules are cleaved into siRNA by Dicer, thus generating a diversepopulation of duplexes that can potentially cover the entire transcript.While some fraction of these molecules are non-functional (i.e. inducelittle or no silencing) one or more have the potential to be highlyfunctional, thereby silencing the gene of interest and alleviating theneed for siRNA design. Unfortunately, due to the interferon response,this same approach is unavailable for mammalian systems. While thiseffect can be circumvented by bypassing the Dicer cleavage step anddirectly introducing siRNA, this tactic carries with it the risk thatthe chosen siRNA sequence may be non-functional or semi-functional.

A number of researches have expressed the view that siRNA design is nota crucial element of RNAi. On the other hand, others in the field havebegun to explore the possibility that RNAi can be made more efficient bypaying attention to the design of the siRNA. Unfortunately, none of thereported methods have provided a satisfactory scheme for reliablyselecting siRNA with acceptable levels of functionality. Accordingly,there is a need to develop rational criteria by which to select siRNAwith an acceptable level of functionality, and to identify siRNA thathave this improved level of functionality, as well as to identify siRNAsthat are hyperfunctional.

SUMMARY OF THE INVENTION

The present invention is directed to increasing the efficiency of RNAi,particularly in mammalian systems. Accordingly, the present inventionprovides kits, siRNAs and methods for increasing siRNA efficacy.

According to one embodiment, the present invention provides a kit forgene silencing, wherein said kit is comprised of a pool of at least twosiRNA duplexes, each of which is comprised of a sequence that iscomplementary to a portion of the sequence of one or more targetmessenger RNA.

According to a second embodiment, the present invention provides amethod for optimizing RNA interference by using one or more siRNAs thatare optimized according to a formula (or algorithm) selected from:Relative functionality of siRNA=−(GC/3)+(AU ₁₅₋₁₉)−(Tm _(20° C.))*3−(G₁₃)*3−(C ₁₉)+(A ₁₉)*2+(A ₃)+(U ₁₀)+(A ₁₄)−(U ₅)−(A ₁₁)  Formula IRelative functionality of siRNA=−(GC/3)−(AU ₁₅₋₁₉)*3−(G ₁₃)*3−(C ₁₉)+(A₁₉)*2+(A ₃)  Formula IIRelative functionality of siRNA=−(GC/3)+(AU ₁₅₋₁₉)−(Tm_(20° C.))*3  Formula IIIRelative functionality of siRNA=−GC/2+(AU ₁₅₋₁₉)/2−(Tm _(20° C.))*2−(G₁₃)*3−(C ₁₉)+(A ₁₉)*2+(A ₃)+(U ₁₀)+(A ₁₄)−(U ₅)−(A ₁₁)  Formula IVRelative functionality of siRNA=−(G ₁₃)*3−(C ₁₉)+(A ₁₉)*2+(A ₃)+(U₁₀)+(A ₁₄)−(U ₅)−(A ₁₁)  Formula VRelative functionality of siRNA=−(G ₁₃)*3−(C ₁₉)+(A ₁₉)*2+(A ₃)  FormulaVIRelative functionality of siRNA=−(GC/2)+(AU ₁₅₋₁₉)/2−(Tm _(20° C.))*1−(G₁₃)*3−(C ₁₉)+(A ₁₉)*3+(A ₃)*3+(U ₁₀)/2+(A ₁₄)/2−(U ₅)/2−(A₁₁)/2  Formula VIIwherein in Formulas I-VII:

Tm_(20° C.)=1 if the Tm is greater than 20° C.;

A₁₉=1 if A is the base at position 19 on the sense strand, otherwise itsvalue is 0;

AU₁₅₋₁₉=0-5 depending on the number of A or U bases on the sense strandat positions 15-19;

G₁₃=1 if G is the base at position 13 on the sense strand, otherwise itsvalue is 0;

C₁₉=1 if C is the base at position 19 of the sense strand, otherwise itsvalue is 0;

GC=the number of G and C bases in the entire sense strand;

A₃=1 if A is the base at position 3 on the sense strand, otherwise itsvalue is 0;

A₁₁=1 if A is the base at position 11 on the sense strand, otherwise itsvalue is 0;

A₁₄=1 if A is the base at position 14 on the sense strand, otherwise itsvalue is 0;

U₁₀=1 if U is the base at position 10 on the sense strand, otherwise itsvalue is 0;

U₅=1 if U is the base at position 5 on the sense strand, otherwise itsvalue is 0; orRelative functionality of siRNA=(−14)*G ₁₃−13*A ₁−12*U ₇−11*U ₂−10*A₁₁−10*U ₄−10*C ₃−10*C ₅−10*C ₆−9*A ₁₀−9*U ₉−9*C ₁₈−8*G ₁₀−7*U ₁−7*U₁₆−7*C ₁₇−7*C ₁₉+7*U ₁₇+8*A ₂+8*A ₄+8*A ₅+8*C ₄+9*G ₈+10*A ₇+10*U₁₈+11*A ₁₉+11*C ₉+15*G ₁+18*A ₃+19*U ₁₀ −Tm−3*(GC _(total))−6*(GC₁₅₋₁₉)−30*X; and  Formula VIIIRelative functionality of siRNA=(14.1)*A ₃+(14.9)*A ₆+(17.6)*A₁₃+(24.7)*A ₁₉+(14.2)*U ₁₀+(10.5)*C ₉+(23.9)*G ₁+(16.3)*G ₂+(−12.3)*A₁₁+(−19.3)*U ₁+(−12.1)*U ₂+(−11)*U ₃+(−15.2)*U ₁₅+(−11.3)*U ₁₆+(−11.8)*C₃+(−17.4)*C ₆+(−10.5)*C ₇+(−13.7)*G ₁₃+(−25.9)*G ₁₉ −Tm−3*(GC_(total))−6*(GC ₁₅₋₁₉)−30*X  Formula IXwherein

-   -   A₁=1 if A is the base at position 1 of the sense strand,        otherwise its value is 0;    -   A₂=1 if A is the base at position 2 of the sense strand,        otherwise its value is 0;    -   A₃=1 if A is the base at position 3 of the sense strand,        otherwise its value is 0;    -   A₄=1 if A is the base at position 4 of the sense strand,        otherwise its value is 0;    -   A₅=1 if A is the base at position 5 of the sense strand,        otherwise its value is 0;    -   A₆=1 if A is the base at position 6 of the sense strand,        otherwise its value is 0;    -   A₇=1 if A is the base at position 7 of the sense strand,        otherwise its value is 0;    -   A₁₀=1 if A is the base at position 10 of the sense strand,        otherwise its value is 0;    -   A₁₁=1 if A is the base at position 11 of the sense strand,        otherwise its value is 0;    -   A₁₃=1 if A is the base at position 13 of the sense strand,        otherwise its value is 0;    -   A₁₉=1 if A is the base at position 19 of the sense strand,        otherwise if another base is present or the sense strand is only        18 base pairs in length, its value is 0;    -   C₃=1 if C is the base at position 3 of the sense strand,        otherwise its value is 0;    -   C₄=1 if C is the base at position 4 of the sense strand,        otherwise its value is 0;    -   C₅=1 if C is the base at position 5 of the sense strand,        otherwise its value is 0;    -   C₆=1 if C is the base at position 6 of the sense strand,        otherwise its value is 0;    -   C₇=1 if C is the base at position 7 of the sense strand,        otherwise its value is 0;    -   C₉=1 if C is the base at position 9 of the sense strand,        otherwise its value is 0;    -   C₁₇=1 if C is the base at position 17 of the sense strand,        otherwise its value is 0;    -   C₁₈=1 if C is the base at position 18 of the sense strand,        otherwise its value is 0;    -   C₁₉=1 if C is the base at position 19 of the sense strand,        otherwise if another base is present or the sense strand is only        18 base pairs in length, its value is 0;    -   G₁=1 if G is the base at position 1 on the sense strand,        otherwise its value is 0;    -   G₂=1 if G is the base at position 2 of the sense strand,        otherwise its value is 0;    -   G₈=1 if G is the base at position 8 on the sense strand,        otherwise its value is 0;    -   G₁₀=1 if G is the base at position 10 on the sense strand,        otherwise its value is 0;    -   G₁₃=1 if G is the base at position 13 on the sense strand,        otherwise its value is 0;    -   G₁₉=1 if G is the base at position 19 of the sense strand,        otherwise if another base is present or the sense strand is only        18 base pairs in length, its value is 0;    -   U₁=1 if U is the base at position 1 on the sense strand,        otherwise its value is 0;    -   U₂=1 if U is the base at position 2 on the sense strand,        otherwise its value is 0;    -   U₃=1 if U is the base at position 3 on the sense strand,        otherwise its value is 0;    -   U₄=1 if U is the base at position 4 on the sense strand,        otherwise its value is 0;    -   U₇=1 if U is the base at position 7 on the sense strand,        otherwise its value is 0;    -   U₉=1 if U is the base at position 9 on the sense strand,        otherwise its value is 0;    -   U₁₀=1 if U is the base at position 10 on the sense strand,        otherwise its value is 0;    -   U₁₅=1 if U is the base at position 15 on the sense strand,        otherwise its value is 0;    -   U₁₆=1 if U is the base at position 16 on the sense strand,        otherwise its value is 0;    -   U₁₇=1 if U is the base at position 17 on the sense strand,        otherwise its value is 0;    -   U₁₈=1 if U is the base at position 18 on the sense strand,        otherwise its value is 0;    -   GC₁₅₋₁₉=the number of G and C bases within positions 15-19 of        the sense strand or within positions 15-18 if the sense strand        is only 18 base pairs in length;    -   GC_(total)=the number of G and C bases in the sense strand;    -   Tm=100 if the targeting site contains an inverted repeat longer        than 4 base pairs, otherwise its value is 0; and    -   X=the number of times that the same nucleotide repeats four or        more times in a row.

According to a third embodiment, the present invention is directed to akit comprised of at least one siRNA that contains a sequence that isoptimized according to one of the formulas above. Preferably the kitcontains at least two optimized siRNA, each of which comprises a duplex,wherein one strand of each duplex comprises at least eighteen contiguousbases that are complementary to a region of a target messenger RNA. Formammalian systems, the siRNA preferably comprises between 18 and 30nucleotide base pairs.

The ability to use the above algorithms, which are not sequence orspecies specific, allows for the cost-effective selection of optimizedsiRNAs for specific target sequences. Accordingly, there will be bothgreater efficiency and reliability in the use of siRNA technologies.

According to a fourth embodiment, the present invention provides amethod for developing an siRNA algorithm for selecting functional andhyperfunctional siRNAs for a given sequence. The method comprises:

-   -   (a) selecting a set of siRNAs;    -   (b) measuring the gene silencing ability of each siRNA from said        set;    -   (c) determining the relative functionality of each siRNA;    -   (d) determining the amount of improved functionality by the        presence or absence of at least one variable selected from the        group consisting of the total GC content, melting temperature of        the siRNA, GC content at positions 15-19, the presence or        absence of a particular nucleotide at a particular position and        the number of times that the same nucleotide repeats within a        given sequence; and    -   (e) developing an algorithm using the information of step (d).

According to this embodiment, preferably the set of siRNAs comprises atleast 90 siRNAs from at least one gene, more preferably at least 180siRNAs from at least two different genes, and most preferably at least270 and 360 siRNAs from at least three and four different genes,respectively. Additionally, in step (d) the determination is made withpreferably at least two, more preferably at least three, even morepreferably at least four, and most preferably all of the variables. Theresulting algorithm is not target sequence specific.

In a fifth embodiment, the present invention provides rationallydesigned siRNAs identified using the formulas above.

In a sixth embodiment, the present invention is directed tohyperfunctional siRNA.

For a better understanding of the present invention together with otherand further advantages and embodiments, reference is made to thefollowing description taken in conjunction with the examples, the scopeof which is set forth in the appended claims.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 shows a model for siRNA-RISC interactions. RISC has the abilityto interact with either end of the siRNA or miRNA molecule. Followingbinding, the duplex is unwound, and the relevant target is identified,cleaved, and released.

FIG. 2 is a representation of the functionality of two hundred andseventy siRNA duplexes that were generated to target human cyclophilin,human diazepam-binding inhibitor (DB), and firefly luciferase.

FIG. 3A is a representation of the silencing effect of 30 siRNAs inthree different cells lines, HEK293, DU145, and Hela. FIG. 3B shows thefrequency of different functional groups (>95% silencing (black), >80%silencing (gray), >50% silencing (dark gray), and <50% silencing(white)) based on GC content. In cases where a given bar is absent froma particular GC percentage, no siRNA were identified for that particulargroup. FIG. 3C shows the frequency of different functional groups basedon melting temperature (Tm). Again, each group has four differentdivisions: >95% (black), >80% (gray), >50% (dark gray), and <50% (white)silencing.

FIGS. 4A-4E are representations of a statistical analysis that revealedcorrelations between silencing and five sequence-related properties ofsiRNA: (4A) an A at position 19 of the sense strand, (4B) an A atposition 3 of the sense strand, (4C) a U at position 10 of the sensestrand, (4D) a base other than G at position 13 of the sense strand, and(4E) a base other than C at position 19 of the sense strand. Allvariables were correlated with siRNA silencing of firefly luciferase andhuman cyclophilin. SiRNAs satisfying the criterion are grouped on theleft (Selected) while those that do not, are grouped on the right(Eliminated). Y-axis is “% Silencing of Control.” Each position on theX-axis represents a unique siRNA.

FIGS. 5 A and 5 B are representations of firefly luciferase andcyclophilin siRNA panels sorted according to functionality and predictedvalues using Formula VIII. The siRNA found within the circle representthose that have Formula VIII values (SMARTscores™) above zero. SiRNAoutside the indicated area have calculated Formula VIII values that arebelow zero. Y-axis is “Expression (% Control).” Each position on theX-axis represents a unique siRNA.

FIG. 6A is a representation of the average internal stability profile(AISP) derived from 270 siRNAs taken from three separate genes(cyclophilin B, DBI and firefly luciferase). Graphs represent AISPvalues of highly functional, functional, and non-functional siRNA. FIG.6B is a comparison between the AISP of naturally derived GFP siRNA(filled squares) and the AISP of siRNA from cyclophilin B, DBI, andluciferase having >90% silencing properties (no fill) for the antisensestrand. “DG” is the symbol for ΔG, free energy.

FIG. 7 is a histogram showing the differences in duplex functionalityupon introduction of basepair mismatches. The X-axis shows the mismatchintroduced inot the siRNA and the position it is introduced (e.g., 8C->Areveals that position 8 (which normally has a C) has been changed to anA). The Y-axis is “% Silencing (Normalized to Control).”

FIG. 8 is histogram that shows the effects of 5′sense and antisensestrand modification with 2′-O-methylation on functionality.

FIG. 9 shows a graph of SMARTscores™ versus RNAi silencing values formore than 360 siRNA directed against 30 different genes. SiRNA to theright of the vertical bar represent those siRNA that have desirableSMARTscores™.

FIGS. 10A-E compare the RNAi of five different genes (SEAP, DBI, PLK,Firefly Luciferase, and Renila Luciferase) by varying numbers ofrandomly selected siRNA and four rationally designed (SMART-selected)siRNA chosen using the algorithm described in Formula VIII. In addition,RNAi induced by a pool of the four SMART-selected siRNA is reported attwo different concentrations (100 and 400 nM). 10F is a comparisonbetween a pool of randomly selected EGFR siRNA (Pool 1) and a pool ofSMART selected EGFR siRNA (Pool 2). Pool 1, S1-S4 and Pool 2 S1-S4represent the individual members that made up each respective pool. Notethat numbers for random siRNAs represent the position of the 5′ end ofthe sense strand of the duplex. The Y-axis represents the % expressionof the control(s). The X-axis is the percent expression of the control.

FIG. 11 shows the Western blot results from cells treated with siRNAdirected against twelve different genes involved in theclathrin-dependent endocytosis pathway (CHC, DynII, CALM, CLCa, CLCb,Eps15, Eps15R, Rab5a, Rab5b, Rab5c, β2 subunit of AP-2 and EEA.1). SiRNAwere selected using Formula VIII. “Pool” represents a mixture ofduplexes 1-4. Total concentration of each siRNA in the pool is 25 nM.Total concentration=4×25=100 nM.

FIG. 12 is a representation of the gene silencing capabilities ofrationally-selected siRNA directed against ten different genes (humanand mouse cyclophilin, C-myc, human lamin A/C, QB (ubiquinol-cytochromec reductase core protein I), MEK1 and MEK2, ATE1 (arginyl-tRNA proteintransferase), GAPDH, and Eg5). The Y-axis is the percent expression ofthe control. Numbers 1, 2, 3 and 4 represent individual rationallyselected siRNA. “Pool” represents a mixture of the four individualsiRNA.

FIG. 13 is the sequence of the top ten Bcl2 siRNAs as determined byFormula VIII. Sequences are listed 5′ to 3′.

FIG. 14 is the knockdown by the top ten Bcl2 siRNAs at 100 nMconcentrations. The Y-axis represents the amount of expression relativeto the non-specific (ns) and transfection mixture control.

FIG. 15 represents a functional walk where siRNA beginning on everyother base pair of a region of the luciferase gene are tested for theability to silence the luciferase gene. The Y-axis represents thepercent expression relative to a control. The X-axis represents theposition of each individual siRNA. Reading from left to right across theX-axis, the position designations are 1, 3, 5, 7, 9, 11, 13, 15, 17, 19,21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55,57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, andPlasmid.

FIGS. 16A and 16B are histograms demonstrating the inhibition of targetgene expression by pools of 2 (16A) and 3 (16B) siRNA duplexes takenfrom the walk described in FIG. 15. The Y-axis in each represents thepercent expression relative to control. The X-axis in each representsthe position of the first siRNA in paired pools, or trios of siRNAs. Forinstance, the first paired pool contains siRNAs 1 and 3. The secondpaired pool contains siRNAs 3 and 5. Pool 3 (of paired pools) containssiRNAs 5 and 7, and so on. For each of 16A and 16B, the X-axis from leftto right reads 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29,31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65,67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, and Plasmid.

FIGS. 17A and 17B are histograms demonstrating the inhibition of targetgene expression by pools of 4 (17A) and 5 (17B) siRNA duplexes. TheY-axis in each represents the percent expression relative to control.The X-axis in each represents the position of the first siRNA in eachpool. For each of 17A and 17B, the X-axis from left to right reads 1, 3,5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41,43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77,79, 81, 83, 85, 87, 89, and Plasmid.

FIGS. 18A and 18B are histograms demonstrating the inhibition of targetgene expression by siRNAs that are ten (18A) and twenty (18B) base pairsapart. The Y-axis represents the percent expression relative to acontrol. The X-axis represents the position of the first siRNA in eachpool. For each of 18A and 18B, the X-axis from left to right reads 1, 3,5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41,43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77,79, 81, 83, 85, 87, 89, and Plasmid.

FIG. 19 shows that pools of siRNAs (dark gray bar) work as well (orbetter) than the best siRNA in the pool (light gray bar). The Y-axisrepresents the percent expression relative to a control. The X-axisrepresents the position of the first siRNA in each pool. The X-axis fromleft to right reads 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27,29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63,65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, and Plasmid.

FIG. 20 shows that the combination of several semifunctional siRNAs(dark gray) result in a significant improvement of gene expressioninhibition over individual (semi-functional; light gray) siRNA. TheY-axis represents the percent expression relative to a control.

FIG. 21 shows both pools (Library, Lib) and individual siRNAs ininhibition of gene expression of Beta-Galactosidase, Renilla Luciferaseand SEAP (alkaline phosphatase). Numbers on the X-axis indicate theposition of the 5′-most nucleotide of the sense strand of the duplex.The Y-axis represents the percent expression of each gene relative to acontrol. Libraries contain siRNAs that begin at the followingnucleotides: Seap: Lib 1: 206, 766, 812, 923, Lib 2: 1117, 1280, 1300,1487, Lib 3: 206, 766, 812, 923, 1117, 1280, 1300, 1487, Lib 4: 206,812, 1117, 1300, Lib 5: 766, 923, 1280, 1487, Lib 6: 206, 1487; Bgal:Lib 1: 979, 1339, 2029, 2590, Lib 2: 1087, 1783, 2399, 3257, Lib 3: 979,1783, 2590, 3257, Lib 4: 979, 1087, 1339, 1783, 2029, 2399, 2590, 3257,Lib 5: 979, 1087, 1339, 1783, Lib 6: 2029, 2399, 2590, 3257; Renilla:Lib 1: 174, 300, 432, 568, Lib 2: 592, 633, 729, 867, Lib 3: 174, 300,432, 568, 592, 633, 729, 867, Lib 4: 174, 432, 592, 729, Lib 5: 300,568, 633, 867, Lib 6: 592, 568.

FIG. 22 shows the results of an EGFR and TfnR internalization assay whensingle gene knockdowns are performed. The Y-axis represents percentinternalization relative to control.

FIG. 23 shows the results of an EGFR and TfnR internalization assay whenmultiple genes are knocked down (e.g. Rab5a, b, c). The Y-axisrepresents the percent internalization relative to control.

FIG. 24 shows the simultaneous knockdown of four different genes. SiRNAsdirected against G6PD, GAPDH, PLK, and UBQ were simultaneouslyintroduced into cells. Twenty-four hours later, cultures were harvestedand assayed for mRNA target levels for each of the four genes. Acomparison is made between cells transfected with individual siRNAs vs.a pool of siRNAs directed against all four genes.

FIG. 25 shows the functionality of ten siRNAs at 0.3 nM concentrations.

DETAILED DESCRIPTION Definitions

Unless stated otherwise, the following terms and phrases have themeanings provided below:

siRNA

The term “siRNA” refers to small inhibitory RNA duplexes that induce theRNA interference (RNAi) pathway. These molecules can vary in length(generally between 18-30 basepairs) and contain varying degrees ofcomplementarity to their target mRNA in the antisense strand. Some, butnot all, siRNA have unpaired overhanging bases on the 5′ or 3′ end ofthe sense strand and/or the antisense strand. The term “siRNA” includesduplexes of two separate strands, as well as single strands that canform hairpin structures comprising a duplex region.

SiRNA may be divided into five (5) groups (non-functional,semi-functional, functional, highly functional, and hyper-functional)based on the level or degree of silencing that they induce in culturedcell lines. As used herein, these definitions are based on a set ofconditions where the siRNA is transfected into said cell line at aconcentration of 100 nM and the level of silencing is tested at a timeof roughly 24 hours after transfection, and not exceeding 72 hours aftertransfection. In this context, “non-functional siRNA” are defined asthose siRNA that induce less than 50% (<50%) target silencing.“Semi-functional siRNA” induce 50-79% target silencing. “FunctionalsiRNA” are molecules that induce 80-95% gene silencing.“Highly-functional siRNA” are molecules that induce greater than 95%gene silencing. “Hyperfunctional siRNA” are a special class ofmolecules. For purposes of this document, hyperfunctional siRNA aredefined as those molecules that: (1) induce greater than 95% silencingof a specific target when they are transfected at subnanomolarconcentrations (i.e., less than one nanomolar); and/or (2) inducefunctional (or better) levels of silencing for greater than 96 hours.These relative functionalities (though not intended to be absolutes) maybe used to compare siRNAs to a particular target for applications suchas functional genomics, target identification and therapeutics.

miRNA

The term “miRNA” refers to microRNA.

Gene Silencing

The phrase “gene silencing” refers to a process by which the expressionof a specific gene product is lessened or attenuated. Gene silencing cantake place by a variety of pathways. Unless specified otherwise, as usedherein, gene silencing refers to decreases in gene product expressionthat results from RNA interference (RNAi), a defined, though partiallycharacterized pathway whereby small inhibitory RNA (siRNA) act inconcert with host proteins (e.g. the RNA induced silencing complex,RISC) to degrade messenger RNA (mRNA) in a sequence-dependent fashion.The level of gene silencing can be measured by a variety of means,including, but not limited to, measurement of transcript levels byNorthern Blot Analysis, B-DNA techniques, transcription-sensitivereporter constructs, expression profiling (e.g. DNA chips), and relatedtechnologies. Alternatively, the level of silencing can be measured byassessing the level of the protein encoded by a specific gene. This canbe accomplished by performing a number of studies including WesternAnalysis, measuring the levels of expression of a reporter protein thathas e.g. fluorescent properties (e.g. GFP) or enzymatic activity (e.g.alkaline phosphatases), or several other procedures.

Transfection

The term “transfection” refers to a process by which agents areintroduced into a cell. The list of agents that can be transfected islarge and includes, but is not limited to, siRNA, sense and/oranti-sense sequences, DNA encoding one or more genes and organized intoan expression plasmid, proteins, protein fragments, and more. There aremultiple methods for transfecting agents into a cell including, but notlimited to, electroporation, calcium phosphate-based transfections,DEAE-dextran-based transfections, lipid-based transfections, molecularconjugate-based transfections (e.g. polylysine-DNA conjugates),microinjection and others.

Target

The term “target” is used in a variety of different forms throughoutthis document and is defined by the context in which it is used. “TargetmRNA” refers to a messenger RNA to which a given siRNA can be directedagainst. “Target sequence” and “target site” refer to a sequence withinthe mRNA to which the sense strand of an siRNA shows varying degrees ofhomology and the antisense strand exhibits varying degrees ofcomplementarity. The term “siRNA target” can refer to the gene, mRNA, orprotein against which an siRNA is directed. Similarly “target silencing”can refer to the state of a gene, or the corresponding mRNA or protein.

Off-Target Silencing and Off-Target Interference

The phrases “off-target silencing” and “off-target interference” aredefined as degradation of mRNA other than the intended target mRNA dueto overlapping and/or partial homology with secondary mRNA messages.

SMARTscore™

The term “SMARTscore™” refers to a number determined by applying any ofthe Formulas I-Formula IX to a given siRNA sequence. The term“SMART-selected” or “rationally selected” or “rational selection” refersto siRNA that have been selected on the basis of their SMARTscores™.

Complementary

The term “complementary” refers to the ability of polynucleotides toform base pairs with one another. Base pairs are typically formed byhydrogen bonds between nucleotide units in antiparallel polynucleotidestrands. Complementary polynucleotide strands can base pair in theWatson-Crick manner (e.g., A to T, A to U, C to G), or in any othermanner that allows for the formation of duplexes. As persons skilled inthe art are aware, when using RNA as opposed to DNA, uracil rather thanthymine is the base that is considered to be complementary to adenosine.However, when a U is denoted in the context of the present invention,the ability to substitute a T is implied, unless otherwise stated.

Perfect complementarity or 100% complementarity refers to the situationin which each nucleotide unit of one polynucleotide strand can hydrogenbond with a nucleotide unit of a second polynucleotide strand. Less thanperfect complementarity refers to the situation in which some, but notall, nucleotide units of two strands can hydrogen bond with each other.For example, for two 20-mers, if only two base pairs on each strand canhydrogen bond with each other, the polynucleotide strands exhibit 10%complementarity. In the same example, if 18 base pairs on each strandcan hydrogen bond with each other, the polynucleotide strands exhibit90% complementarity.

Deoxynucleotide

The term “deoxynucleotide” refers to a nucleotide or polynucleotidelacking a hydroxyl group (OH group) at the 2′ and/or 3′ position of asugar moiety. Instead, it has a hydrogen bonded to the 2′ and/or 3′carbon. Within an RNA molecule that comprises one or moredeoxynucleotides, “deoxynucleotide” refers to the lack of an OH group atthe 2′ position of the sugar moiety, having instead a hydrogen bondeddirectly to the 2′ carbon.

Deoxyribonucleotide

The terms “deoxyribonucleotide” and “DNA” refer to a nucleotide orpolynucleotide comprising at least one sugar moiety that has an H,rather than an OH, at its 2′ and/or 3′position.

Substantially Similar

The phrase “substantially similar” refers to a similarity of at least90% with respect to the identity of the bases of the sequence.

Duplex Region

The phrase “duplex region” refers to the region in two complementary orsubstantially complementary polynucleotides that form base pairs withone another, either by Watson-Crick base pairing or any other mannerthat allows for a stabilized duplex between polynucleotide strands thatare complementary or substantially complementary. For example, apolynucleotide strand having 21 nucleotide units can base pair withanother polynucleotide of 21 nucleotide units, yet only 19 bases on eachstrand are complementary or substantially complementary, such that the“duplex region” has 19 base pairs. The remaining bases may, for example,exist as 5′ and 3′ overhangs. Further, within the duplex region, 100%complementarity is not required; substantial complementarity isallowable within a duplex region. For example, a mismatch in a duplexregion consisting of 19 base pairs results in 94.7% complementarity,rendering the duplex region substantially complementary.

Nucleotide

The term “nucleotide” refers to a ribonucleotide or adeoxyribonucleotide or modified form thereof, as well as an analogthereof. Nucleotides include species that comprise purines, e.g.,adenine, hypoxanthine, guanine, and their derivatives and analogs, aswell as pyrimidines, e.g., cytosine, uracil, thymine, and theirderivatives and analogs.

Nucleotide analogs include nucleotides having modifications in thechemical structure of the base, sugar and/or phosphate, including, butnot limited to, 5-position pyrimidine modifications, 8-position purinemodifications, modifications at cytosine exocyclic amines, andsubstitution of 5-bromo-uracil; and 2′-position sugar modifications,including but not limited to, sugar-modified ribonucleotides in whichthe 2′-OH is replaced by a group such as an H, OR, R, halo, SH, SR, NH₂,NHR, NR₂, or CN, wherein R is an alkyl moiety. Nucleotide analogs arealso meant to include nucleotides with bases such as inosine, queuosine,xanthine, sugars such as 2′-methyl ribose, non-natural phosphodiesterlinkages such as methylphosphonates, phosphorothioates and peptides.

Modified bases refer to nucleotide bases such as, for example, adenine,guanine, cytosine, thymine, uracil, xanthine, inosine, and queuosinethat have been modified by the replacement or addition of one or moreatoms or groups. Some examples of types of modifications that cancomprise nucleotides that are modified with respect to the base moietiesinclude but are not limited to, alkylated, halogenated, thiolated,aminated, amidated, or acetylated bases, individually or in combination.More specific examples include, for example, 5-propynyluridine,5-propynylcytidine, 6-methyladenine, 6-methylguanine,N,N,-dimethyladenine, 2-propyladenine, 2-propylguanine, 2-aminoadenine,1-methylinosine, 3-methyluridine, 5-methylcytidine, 5-methyluridine andother nucleotides having a modification at the 5 position,5-(2-amino)propyl uridine, 5-halocytidine, 5-halouridine,4-acetylcytidine, 1-methyladenosine, 2-methyladenosine,3-methylcytidine, 6-methyluridine, 2-methylguanosine, 7-methylguanosine,2,2-dimethylguanosine, 5-methylaminoethyluridine, 5-methyloxyuridine,deazanucleotides such as 7-deaza-adenosine, 6-azouridine, 6-azocytidine,6-azothymidine, 5-methyl-2-thiouridine, other thio bases such as2-thiouridine and 4-thiouridine and 2-thiocytidine, dihydrouridine,pseudouridine, queuosine, archaeosine, naphthyl and substituted naphthylgroups, any O- and N-alkylated purines and pyrimidines such asN6-methyladenosine, 5-methylcarbonylmethyluridine, uridine 5-oxyaceticacid, pyridine-4-one, pyridine-2-one, phenyl and modified phenyl groupssuch as aminophenol or 2,4,6-trimethoxy benzene, modified cytosines thatact as G-clamp nucleotides, 8-substituted adenines and guanines,5-substituted uracils and thymines, azapyrimidines, carboxyhydroxyalkylnucleotides, carboxyalkylaminoalkyl nucleotides, andalkylcarbonylalkylated nucleotides. Modified nucleotides also includethose nucleotides that are modified with respect to the sugar moiety, aswell as nucleotides having sugars or analogs thereof that are notribosyl. For example, the sugar moieties may be, or be based on,mannoses, arabinoses, glucopyranoses, galactopyranoses, 4′-thioribose,and other sugars, heterocycles, or carbocycles.

The term nucleotide is also meant to include what are known in the artas universal bases. By way of example, universal bases include but arenot limited to 3-nitropyrrole, 5-nitroindole, or nebularine. The term“nucleotide” is also meant to include the N3′ to P5′ phosphoramidate,resulting from the substitution of a ribosyl 3′ oxygen with an aminegroup.

Further, the term nucleotide also includes those species that have adetectable label, such as for example a radioactive or fluorescentmoiety, or mass label attached to the nucleotide.

Polynucleotide

The term “polynucleotide” refers to polymers of nucleotides, andincludes but is not limited to DNA, RNA, DNA/RNA hybrids includingpolynucleotide chains of regularly and/or irregularly alternatingdeoxyribosyl moieties and ribosyl moieties (i.e., wherein alternatenucleotide units have an —OH, then and —H, then an —OH, then an —H, andso on at the 2′ position of a sugar moiety), and modifications of thesekinds of polynucleotides, wherein the attachment of various entities ormoieties to the nucleotide units at any position are included.

Polyribonucleotide

The term “polyribonucleotide” refers to a polynucleotide comprising twoor more modified or unmodified ribonucleotides and/or their analogs. Theterm “polyribonucleotide” is used interchangeably with the term“oligoribonucleotide.”

Ribonucleotide and Ribonucleic Acid

The term “ribonucleotide” and the phrase “ribonucleic acid” (RNA), referto a modified or unmodified nucleotide or polynucleotide comprising atleast one ribonucleotide unit. A ribonucleotide unit comprises anhydroxyl group attached to the 2′ position of a ribosyl moiety that hasa nitrogenous base attached in N-glycosidic linkage at the 1′ positionof a ribosyl moiety, and a moiety that either allows for linkage toanother nucleotide or precludes linkage.

DETAILED DESCRIPTION OF THE INVENTION

The present invention is directed to improving the efficiency of genesilencing by siRNA. Through the inclusion of multiple siRNA sequencesthat are targeted to a particular gene and/or selecting an siRNAsequence based on certain defined criteria, improved efficiency may beachieved.

The present invention will now be described in connection with preferredembodiments. These embodiments are presented in order to aid in anunderstanding of the present invention and are not intended, and shouldnot be construed, to limit the invention in any way. All alternatives,modifications and equivalents that may become apparent to those ofordinary skill upon reading this disclosure are included within thespirit and scope of the present invention.

Furthermore, this disclosure is not a primer on RNA interference. Basicconcepts known to persons skilled in the art have not been set forth indetail.

Optimizing siRNA

According to one embodiment, the present invention provides a method forimproving the effectiveness of gene silencing for use to silence aparticular gene through the selection of an optimal siRNA. An siRNAselected according to this method may be used individually, or inconjunction with the first embodiment, i.e., with one or more othersiRNAs, each of which may or may not be selected by this criteria inorder to maximize their efficiency.

The degree to which it is possible to select an siRNA for a given mRNAthat maximizes these criteria will depend on the sequence of the mRNAitself. However, the selection criteria will be independent of thetarget sequence. According to this method, an siRNA is selected for agiven gene by using a rational design. That said, rational design can bedescribed in a variety of ways. Rational design is, in simplest terms,the application of a proven set of criteria that enhance the probabilityof identifying a functional or hyperfunctional siRNA. In one method,rationally designed siRNA can be identified by maximizing one or more ofthe following criteria:

-   -   1. A low GC content, preferably between about 30-52%.    -   2. At least 2, preferably at least 3 A or U bases at positions        15-19 of the siRNA on the sense strand.    -   3. An A base at position 19 of the sense strand.    -   4. An A base at position 3 of the sense strand.    -   5. A U base at position 10 of the sense strand.    -   6. An A base at position 14 of the sense strand.    -   7. A base other than C at position 19 of the sense strand.    -   8. A base other than G at position 13 of the sense strand.    -   9. A Tm, which refers to the character of the internal repeat        that results in inter- or intramolecular structures for one        strand of the duplex, that is preferably not stable at greater        than 50° C., more preferably not stable at greater than 37° C.,        even more preferably not stable at greater than 30° C. and most        preferably not stable at greater than 20° C.    -   10. A base other than U at position 5 of the sense strand.    -   11. A base other than A at position 11 of the sense strand.

Criteria 5, 6, 10 and 11 are minor criteria, but are nonethelessdesirable.

Accordingly, preferably an siRNA will satisfy as many of theaforementioned criteria as possible, more preferably at least 1-4 and7-9, and most preferably all of the criteria

With respect to the criteria, GC content, as well as a high number of AUin positions 15-19, may be important for easement of the unwinding ofdouble stranded siRNA duplex. Duplex unwinding has been shown to becrucial for siRNA functionality in vivo.

With respect to criterion 9, the internal structure is measured in termsof the melting temperature of the single strand of siRNA, which is thetemperature at which 50% of the molecules will become denatured. Withrespect to criteria 2-8 and 10-11, the positions refer to sequencepositions on the sense strand, which is the strand that is identical tothe mRNA.

In one preferred embodiment, at least criteria 1 and 8 are satisfied. Inanother preferred embodiment, at least criteria 7 and 8 are satisfied.In still another preferred embodiment, at least criteria 1, 8 and 9 aresatisfied.

It should be noted that all of the aforementioned criteria regardingsequence position specifics are with respect to the 5′ end of the sensestrand. Reference is made to the sense strand, because most databasescontain information that describes the information of the mRNA. Becauseaccording to the present invention a chain can be from 18 to 30 bases inlength, and the aforementioned criteria assumes a chain 19 base pairs inlength, it is important to keep the aforementioned criteria applicableto the correct bases.

When there are only 18 bases, the base pair that is not present is thebase pair that is located at the 3′ of the sense strand. When there aretwenty to thirty bases present, then additional bases are added at the5′ end of the sense chain and occupy positions ⁻1 to ⁻11. Accordingly,with respect to SEQ. ID NO. 0001. NNANANNNNUCNAANNNNA and SEQ. ID NO.0028. GUCNNANANNNNUCNAANNNNA, both would have A at position 3, A atposition 5, U at position 10, C at position 11, A and position 13, A andposition 14 and A at position 19. However, SEQ. ID NO. 0028 would alsohave C at position −1, U at position −2 and G at position −3.

For a 19 base pair siRNA, an optimal sequence of one of the strands maybe represented below, where N is any base, A, C, G, or U: SEQ. ID NO.0001. NNANANNNNUCNAANNNNA SEQ. ID NO. 0002. NNANANNNNUGNAANNNNA SEQ. IDNO. 0003. NNANANNNNUUNAANNNNA SEQ. ID NO. 0004. NNANANNNNUCNCANNNNA SEQ.ID NO. 0005. NNANANNNNUGNCANNNNA SEQ. ID NO. 0006. NNANANNNNUUNCANNNNASEQ. ID NO. 0007. NNANANNNNUCNUANNNNA SEQ. ID NO. 0008..NNANANNNNUGNUANNNNA SEQ. ID NO. 0009. NNANANNNNUUNUANNNNA SEQ. ID NO.0010. NNANCNNNNUCNAANNNNA SEQ. ID NO. 0011. NNANCNNNNUGNAANNNNA SEQ. IDNO. 0012. NNANCNNNNUUNAANNNNA SEQ. ID NO. 0013. NNANCNNNNUCNCANNNNA SEQ.ID NO. 0014. NNANCNNNNUGNCANNNNA SEQ. ID NO. 0015. NNANCNNNNUUNCANNNNASEQ. ID NO. 0016. NNANCNNNNUCNUANNNNA SEQ. ID NO. 0017.NNANCNNNNUGNUANNNNA SEQ. ID NO. 0018. NNANCNNNNUUNUANNNNA SEQ. ID NO.0019. NNANGNNNNUCNAANNNNA SEQ. ID NO. 0020. NNANGNNNNUGNAANNNNA SEQ. IDNO. 0021. NNANGNNNNUUNAANNNNA SEQ. ID NO. 0022. NNANGNNNNUCNCANNNNA SEQ.ID NO. 0023. NNANGNNNNUGNCANNNNA SEQ. ID NO. 0024. NNANGNNNNUUNCANNNNASEQ. ID NO. 0025. NNANGNNNNUCNUANNNNA SEQ. ID NO. 0026.NNANGNNNNUGNUANNNNA SEQ. ID NO. 0027. NNANGNNNNUUNUANNNNA

In one embodiment, the sequence used as an siRNA is selected by choosingthe siRNA that score highest according to one of the following sevenalgorithms that are represented by Formulas I-VII:Relative functionality of siRNA=−(GC/3)+(AU ₁₅₋₁₉)−(Tm _(20° C.))*3−(G₁₃)*3−(C ₁₉)+(A ₁₉)*2+(A ₃)+(U ₁₀)+(A ₁₄)−(U ₅)−(A ₁₁)  Formula IRelative functionality of siRNA=−(GC/3)−(AU ₁₅₋₁₉)*3−(G ₁₃)*3−(C ₁₉)+(A₁₉)*2+(A ₃)  Formula IIRelative functionality of siRNA=−(GC/3)+(AU ₁₅₋₁₉)−(Tm_(20° C.))*3  Formula IIIRelative functionality of siRNA=−GC/2+(AU ₁₅₋₁₉)/2−(Tm _(20° C.))*2−(G₁₃)*3−(C ₁₉)+(A ₁₉)*2+(A ₃)+(U ₁₀)+(A ₁₄)−(U ₅)−(A ₁₁)  Formula IVRelative functionality of siRNA=−(G ₁₃)*3−(C ₁₉)+(A ₁₉)*2+(A ₃)+(U₁₀)+(A ₁₄)−(U ₅)−(A ₁₁)  Formula VRelative functionality of siRNA=−(G ₁₃)*3−(C ₁₉)+(A ₁₉)*2+(A ₃)  FormulaVIRelative functionality of siRNA=−(GC/2)+(AU ₁₅₋₁₉)/2−(Tm _(20° C.))*1−(G₁₃)*3−(C ₁₉)+(A ₁₉)*3+(A ₃)*3+(U ₁₀)/2+(A ₁₄)/2−(U ₅)/2−(A₁₁)/2  Formula VIIIn Formulas I-VII:wherein

A₁₉=1 if A is the base at position 19 on the sense strand, otherwise itsvalue is 0,

AU₁₅₋₁₉=0-5 depending on the number of A or U bases on the sense strandat positions 15-19;

G₁₃=1 if G is the base at position 13 on the sense strand, otherwise itsvalue is 0;

C₁₉=1 if C is the base at position 19 of the sense strand, otherwise itsvalue is 0;

GC=the number of G and C bases in the entire sense strand;

Tm_(20° C.)=1 if the Tm is greater than 20° C.;

A₃=1 if A is the base at position 3 on the sense strand, otherwise itsvalue is 0;

U₁₀=1 if U is the base at position 10 on the sense strand, otherwise itsvalue is 0;

A₁₄=1 if A is the base at position 14 on the sense strand, otherwise itsvalue is 0;

U₅=1 if U is the base at position 5 on the sense strand, otherwise itsvalue is 0; and

A₁₁=1 if A is the base at position 11 of the sense strand, otherwise itsvalue is 0.

Formulas I-VII provide relative information regarding functionality.When the values for two sequences are compared for a given formula, therelative functionality is ascertained; a higher positive numberindicates a greater functionality. For example, in many applications avalue of 5 or greater is beneficial.

Additionally, in many applications, more than one of these formulaswould provide useful information as to the relative functionality ofpotential siRNA sequences. However, it is beneficial to have more thanone type of formula, because not every formula will be able to help todifferentiate among potential siRNA sequences. For example, inparticularly high GC mRNAs, formulas that take that parameter intoaccount would not be useful and application of formulas that lack GCelements (e.g., formulas V and VI) might provide greater insights intoduplex functionality. Similarly, formula II might by used in situationswhere hairpin structures are not observed in duplexes, and formula IVmight be applicable for sequences that have higher AU content. Thus, onemay consider a particular sequence in light of more than one or even allof these algorithms to obtain the best differentiation among sequences.In some instances, application of a given algorithm may identify anunusually large number of potential siRNA sequences, and in those cases,it may be appropriate to re-analyze that sequence with a secondalgorithm that is, for instance, more stringent. Alternatively, it isconceivable that analysis of a sequence with a given formula yields noacceptable siRNA sequences (i.e. low SMARTscores™). In this instance, itmay be appropriate to re-analyze that sequences with a second algorithmthat is, for instance, less stringent. In still other instances,analysis of a single sequence with two separate formulas may give riseto conflicting results (i.e. one formula generates a set of siRNA withhigh SMARTscores™ while the other formula identifies a set of siRNA withlow SMARTscores™). In these instances, it may be necessary to determinewhich weighted factor(s) (e.g. GC content) are contributing to thediscrepancy and assessing the sequence to decide whether these factorsshould or should not be included. Alternatively, the sequence could beanalyzed by a third, fourth, or fifth algorithm to identify a set ofrationally designed siRNA.

The above-referenced criteria are particularly advantageous when used incombination with pooling techniques as depicted in Table I: TABLE IFunctional Probability Oligos Pools Criteria >95% >80% <70% >95% >80%<70% Current 33.0 50.0 23.0 79.5 97.3 0.3 New 50.0 88.5 8.0 93.8 99.980.005 (GC) 28.0 58.9 36.0 72.8 97.1 1.6The term “current” refers to Tuschl's conventional siRNA parameters(Elbashir, S. M. et al. (2002) “Analysis of gene function in somaticmammalian cells using small interfering RNAs” Methods 26: 199-213).“New” refers to the design parameters described in Formulas I-VII. “GC”refers to criteria that select siRNA solely on the basis of GC content.

As Table I indicates, when more functional siRNA duplexes are chosen,siRNAs that produce <70% silencing drops from 23% to 8% and the numberof siRNA duplexes that produce >80% silencing rises from 50% to 88.5%.Further, of the siRNA duplexes with >80% silencing, a larger portion ofthese siRNAs actually silence >95% of the target expression (the newcriteria increases the portion from 33% to 50%). Using this new criteriain pooled siRNAs, shows that, with pooling, the amount of silencing >95%increases from 79.5% to 93.8% and essentially eliminates any siRNA poolfrom silencing less than 70%.

Table II similarly shows the particularly beneficial results of poolingin combination with the aforementioned criteria. However, Table II,which takes into account each of the aforementioned variables,demonstrates even a greater degree of improvement in functionality.TABLE II Functional Probability Oligos Pools Non- Non- FunctionalAverage functional Functional Average functional Random 20 40 50 67 97 3Criteria 1 52 99 0.1 97 93 0.0040 Criteria 4 89 99 0.1 99 99 0.0000The terms “functional,” “Average,” and “Non-functional” refer to siRNAthat exhibit >80%, >50%, and <50% functionality, respectively. Criteria1 and 4 refer to specific criteria described above.

The above-described algorithms may be used with or without a computerprogram that allows for the inputting of the sequence of the mRNA andautomatically outputs the optimal siRNA. The computer program may, forexample, be accessible from a local terminal or personal computer, overan internal network or over the Internet.

In addition to the formulas above, more detailed algorithms may be usedfor selecting siRNA. Preferably, at least one RNA duplex of between 18and 30 base pairs is selected such that it is optimized according aformula selected from:(−14)*G₁₃−13*A₁−12*U₇−11*U₂−10*A₁₁−10*U₄−10*C₃−10*C₅−10*C₆−9*A₁₀−9*U₉−9*C₁₈−8*G₁₀−7*U₁−7*U₁₆−7*C₁₇−7*C₁₉+7*U₁₇+8*A₂+8*A₄+8*A₅+8*C₄+9*G₈+10*A₇+10*U₁₈+11*A₁₉+11*C₉+15*G₁+18*A₃+19*U₁₀−Tm−3*(GC_(total))−6*(GC₁₅₋₁₉)−30*X;and  Formula VIII(14.1)*A₃+(14.9)*A₆+(17.6)*A₁₃+(24.7)*A₁₉+(14.2)*U₁₀+(10.5)*C₉+(23.9)*G₁+(16.3)*G₂+(−12.3)*A₁₁+(−19.3)*U₁+(−12.1)*U₂+(−11)*U₃+(−15.2)*U₁₅+(−11.3)*U₁₆+(−11.8)*C₃+(−17.4)*C₆+(−10.5)*C₇+(−13.7)*G₁₃+(−25.9)*G₁₉−Tm−3*(GC_(total))−6*(GC₁₅₋₁₉)−30*X  FormulaIX

wherein

-   -   A₁=1 if A is the base at position 1 of the sense strand,        otherwise its value is 0;    -   A₂=1 if A is the base at position 2 of the sense strand,        otherwise its value is 0;    -   A₃=1 if A is the base at position 3 of the sense strand,        otherwise its value is 0;    -   A₄=1 if A is the base at position 4 of the sense strand,        otherwise its value is 0;    -   A₅=1 if A is the base at position 5 of the sense strand,        otherwise its value is 0;    -   A₆=1 if A is the base at position 6 of the sense strand,        otherwise its value is 0;    -   A₇=1 if A is the base at position 7 of the sense strand,        otherwise its value is 0;    -   A₁₀=1 if A is the base at position 10 of the sense strand,        otherwise its value is 0;    -   A₁₁=1 if A is the base at position 11 of the sense strand,        otherwise its value is 0;    -   A₁₃=1 if A is the base at position 13 of the sense strand,        otherwise its value is 0;    -   A₁₉=1 if A is the base at position 19 of the sense strand,        otherwise if another base is present or the sense strand is only        18 base pairs in length, its value is 0;    -   C₃=1 if C is the base at position 3 of the sense strand,        otherwise its value is 0;    -   C₄=1 if C is the base at position 4 of the sense strand,        otherwise its value is 0;    -   C₅=1 if C is the base at position 5 of the sense strand,        otherwise its value is 0;    -   C₆=1 if C is the base at position 6 of the sense strand,        otherwise its value is 0;    -   C₇=1 if C is the base at position 7 of the sense strand,        otherwise its value is 0;    -   C₉=1 if C is the base at position 9 of the sense strand,        otherwise its value is 0;    -   C₁₇=1 if C is the base at position 17 of the sense strand,        otherwise its value is 0;    -   C₁₈=1 if C is the base at position 18 of the sense strand,        otherwise its value is 0;    -   C₁₉=1 if C is the base at position 19 of the sense strand,        otherwise if another base is present or the sense strand is only        18 base pairs in length, its value is 0;    -   G₁=1 if G is the base at position 1 on the sense strand,        otherwise its value is 0;    -   G₂=1 if G is the base at position 2 of the sense strand,        otherwise its value is 0;    -   G₈=1 if G is the base at position 8 on the sense strand,        otherwise its value is 0;    -   G₁₀=1 if G is the base at position 10 on the sense strand,        otherwise its value is 0;    -   G₁₃=1 if G is the base at position 13 on the sense strand,        otherwise its value is 0;    -   G₁₉=1 if G is the base at position 19 of the sense strand,        otherwise if another base is present or the sense strand is only        18 base pairs in length, its value is 0;    -   U₁=1 if U is the base at position 1 on the sense strand,        otherwise its value is 0;    -   U₂=1 if U is the base at position 2 on the sense strand,        otherwise its value is 0;    -   U₃=1 if U is the base at position 3 on the sense strand,        otherwise its value is 0;    -   U₄=1 if U is the base at position 4 on the sense strand,        otherwise its value is 0;    -   U₇=1 if U is the base at position 7 on the sense strand,        otherwise its value is 0;    -   U₉=1 if U is the base at position 9 on the sense strand,        otherwise its value is 0;    -   U₁₀=1 if U is the base at position 10 on the sense strand,        otherwise its value is 0;    -   U₁₅=1 if U is the base at position 15 on the sense strand,        otherwise its value is 0;    -   U₁₆=1 if U is the base at position 16 on the sense strand,        otherwise its value is 0;    -   U₁₇=1 if U is the base at position 17 on the sense strand,        otherwise its value is 0;    -   U₁₈=1 if U is the base at position 18 on the sense strand,        otherwise its value is 0;    -   GC₁₅₋₁₉=the number of G and C bases within positions 15-19 of        the sense strand, or within positions 15-18 if the sense strand        is only 18 base pairs in length;    -   GC_(total)=the number of G and C bases in the sense strand;    -   Tm=100 if the siRNA oligo has the internal repeat longer then 4        base pairs, otherwise its value is 0; and    -   X=the number of times that the same nucleotide repeats four or        more times in a row.

The above formulas VIII and IX, as well as formulas I-VII, providemethods for selecting siRNA in order to increase the efficiency of genesilencing. A subset of variables of any of the formulas may be used,though when fewer variables are used, the optimization hierarchy becomesless reliable.

With respect to the variables of the above-referenced formulas, a singleletter of A or C or G or U followed by a subscript refers to a binarycondition. The binary condition is that either the particular base ispresent at that particular position (wherein the value is “1”) or thebase is not present (wherein the value is “0”). Because position 19 isoptional, i.e. there might be only 18 base pairs, when there are only 18base pairs, any base with a subscript of 19 in the formulas above wouldhave a zero value for that parameter. Before or after each variable is anumber followed by *, which indicates that the value of the variable isto be multiplied or weighed by that number.

The numbers preceding the variables A, or G, or C, or U in Formulas VIIIand IX (or after the variables in Formula I-VII) were determined bycomparing the difference in the frequency of individual bases atdifferent positions in functional siRNA and total siRNA. Specifically,the frequency in which a given base was observed at a particularposition in functional groups was compared with the frequency that thatsame base was observed in the total, randomly selected siRNA set. If theabsolute value of the difference between the functional and total valueswas found to be greater than 6%, that parameter was included in theequation. Thus for instance, if the frequency of finding a “G” atposition 13 (G₁₃) is found to be 6% in a given functional group, and thefrequency of G₁₃ in the total population of siRNAs is 20%, thedifference between the two values is 6%−20%=−14%. As the absolute valueis greater than six (6), this factor (−14) is included in the equation.Thus in Formula VIII, in cases where the siRNA under study has a G inposition 13, the accrued value is (−14)*(1)=−14. In contrast, when abase other than G is found at position 13, the accrued value is(−14)*(0)=0.

When developing a means to optimize siRNAs, the inventors observed thata bias toward low internal thermodynamic stability of the duplex at the5′-antisense (AS) end is characteristic of naturally occurring miRNAprecursors. The inventors extended this observation to siRNAs for whichfunctionality had been assessed in tissue culture.

With respect to the parameter GC₁₅₋₁₉, a value of 0-5 will be ascribeddepending on the number of G or C bases at positions 15 to 19. If thereare only 18 base pairs, the value is between 0 and 4.

With respect to the criterion GC_(total) content, a number from 0-30will be ascribed, which correlates to the total number of G and Cnucleotides on the sense strand, excluding overhangs. Without wishing tobe bound by any one theory, it is postulated that the significance ofthe GC content (as well as AU content at positions 15-19, which is aparameter for formulas III-VII) relates to the easement of the unwindingof a double-stranded siRNA duplex. Duplex unwinding is believed to becrucial for siRNA functionality in vivo and overall low internalstability, especially low internal stability of the first unwound basepair is believed to be important to maintain sufficient processivity ofRISC complex-induced duplex unwinding. If the duplex has 19 base pairs,those at positions 15-19 on the sense strand will unwind first if themolecule exhibits a sufficiently low internal stability at thatposition. As persons skilled in the art are aware, RISC is a complex ofapproximately twelve proteins; Dicer is one, but not the only, helicasewithin this complex. Accordingly, although the GC parameters arebelieved to relate to activity with Dicer, they are also important foractivity with other RISC proteins.

The value of the parameter Tm is 0 when there are no internal repeatslonger than (or equal to) four base pairs present in the siRNA duplex;otherwise the value is 1. Thus for example, if the sequence ACGUACGU, orany other four nucleotide (or more) palindrome exists within thestructure, the value will be one (1). Alternatively if the structureACGGACG, or any other 3 nucleotide (or less) palindrome exists, thevalue will be zero (0).

The variable “X” refers to the number of times that the same nucleotideoccurs contiguously in a stretch of four or more units. If there are,for example, four contiguous As in one part of the sequence andelsewhere in the sequence four contiguous Cs, X=2. Further, if there aretwo separate contiguous stretches of four of the same nucleotides oreight or more of the same nucleotides in a row, then X=2. However, Xdoes not increase for five, six or seven contiguous nucleotides.

Again, when applying Formula VIII or Formula IX to a given mRNA, (the“target RNA” or “target molecule”), one may use a computer program toevaluate the criteria for every sequence of 18-30 base pairs or onlysequences of a fixed length, e.g., 19 base pairs. Preferably thecomputer program is designed such that it provides a report ranking ofall of the potential siRNAs between 18 and 30 base pairs, rankedaccording to which sequences generate the highest value. A higher valuerefers to a more efficient siRNA for a particular target gene. Thecomputer program that may be used, may be developed in any computerlanguage that is known to be useful for scoring nucleotide sequences, orit may be developed with the assistance of commercially availableproduct such as Microsoft's product .net. Additionally, rather than runevery sequence through one and/or another formula, one may compare asubset of the sequences, which may be desirable if for example only asubset are available. For instance, it may be desirable to first performa BLAST (Basic Local Alignment Search Tool) search and to identifysequences that have no homology to other targets. Alternatively, it maybe desirable to scan the sequence and to identify regions of moderate GCcontext, then perform relevant calculations using one of theabove-described formulas on these regions. These calculations can bedone manually or with the aid of a computer.

As with Formulas I-VII, either Formula VIII or Formula IX may be usedfor a given mRNA target sequence. However, it is possible that accordingto one or the other formula more than one siRNA will have the samevalue. Accordingly, it is beneficial to have a second formula by whichto differentiate sequences. Formula IX was derived in a similar fashionas Formula VIII, yet used a larger data set and thus yields sequenceswith higher statistical correlations to highly functional duplexes. Thesequence that has the highest value ascribed to it may be referred to asa “first optimized duplex.” The sequence that has the second highestvalue ascribed to it may be referred to as a “second optimized duplex.”Similarly, the sequences that have the third and fourth highest valuesascribed to them may be referred to as a third optimized duplex and afourth optimized duplex, respectively. When more than one sequence hasthe same value, each of them may, for example, be referred to as firstoptimized duplex sequences or co-first optimized duplexes.

SiRNA sequences identified using Formula VIII are contained within theenclosed compact disks. The data included on the enclosed compact disksis described more fully below. The sequences identified by Formula VIIIthat are disclosed in the compacts disks may be used in gene silencingapplications.

It should be noted that for Formulas VIII and IX all of theaforementioned criteria are identified as positions on the sense strandwhen oriented in the 5′ to 3′ direction as they are identified inconnection with Formulas I-VII unless otherwise specified.

Formulas I-IX, may be used to select or to evaluate one, or more thanone, siRNA in order to optimize silencing. Preferably, at least twooptimized siRNAs that have been selected according to at least one ofthese formulas are used to silence a gene, more preferably at leastthree and most preferably at least four. The siRNAs may be usedindividually or together in a pool or kit. Further, they may be appliedto a cell simultaneously or separately. Preferably, the at least twosiRNAs are applied simultaneously. Pools are particularly beneficial formany research applications. However, for therapeutics, it may be moredesirable to employ a single hyperfunctional siRNA as describedelsewhere in this application.

When planning to conduct gene silencing, and it is necessary to choosebetween two or more siRNAs, one should do so by comparing the relativevalues when the siRNA are subjected to one of the formulas above. Ingeneral a higher scored siRNA should be used.

Useful applications include, but are not limited to, target validation,gene functional analysis, research and drug discovery, gene therapy andtherapeutics. Methods for using siRNA in these applications are wellknown to persons of skill in the art.

Because the ability of siRNA to function is dependent on the sequence ofthe RNA and not the species into which it is introduced, the presentinvention is applicable across a broad range of species, including butnot limited to all mammalian species, such as humans, dogs, horses,cats, cows, mice, hamsters, chimpanzees and gorillas, as well as otherspecies and organisms such as bacteria, viruses, insects, plants and C.elegans.

The present invention is also applicable for use for silencing a broadrange of genes, including but not limited to the roughly 45,000 genes ofa human genome, and has particular relevance in cases where those genesare associated with diseases such as diabetes, Alzheimer's, cancer, aswell as all genes in the genomes of the aforementioned organisms.

The siRNA selected according to the aforementioned criteria or one ofthe aforementioned algorithms are also, for example, useful in thesimultaneous screening and functional analysis of multiple genes andgene families using high throughput strategies, as well as in directgene suppression or silencing.

Development of the Algorithms

To identify siRNA sequence features that promote functionality and toquantify the importance of certain currently accepted conventionalfactors—such as G/C content and target site accessibility—the inventorssynthesized an siRNA panel consisting of 270 siRNAs targeting threegenes, Human Cyclophilin, Firefly Luciferase, and Human DBI. In allthree cases, siRNAs were directed against specific regions of each gene.For Human Cyclophilin and Firefly Luciferase, ninety siRNAs weredirected against a 199 bp segment of each respective mRNA. For DBI, 90siRNAs were directed against a smaller, 109 base pair region of themRNA. The sequences to which the siRNAs were directed are providedbelow.

It should be noted that in certain sequences, “t” is present. This isbecause many databases contain information in this manner. However, thet denotes a uracil residue in mRNA and siRNA. Any algorithm will, unlessotherwise specified, process at in a sequence as a u.

Human cyclophilin: 193-390 M60857

SEQ. ID NO. 29:

gttccaaaaacagtggataattttgtggccttagctacaggagagaaaggatttggctacaaaaacagcaaattccatcgtgtaatcaaggacttcatgatccagggcggagacttcaccaggggagatggcacaggaggaaagagcatctacggtgagcgcttccccgatgagaacttcaaactgaagcactacgggcctggctggg

Firefly luciferase: 1434-1631, U47298 (pGL3, Promega)

SEQ. ID NO. 30:

tgaacttcccgccgccgttgttgttttggagcacggaaagacgatgacggaaaaagagatcgtggattacgtcgccagtcaagtaacaaccgcgaaaaagttgcgcggaggagttgtgtttgtggacgaagtaccgaaaggtcttaccggaaaactcgacgcaagaaaaatcagagagatcctcataaaggccaagaagg

DBI, NM_(—)020548 (202-310) (every position)

SEQ. ID NO. 0031:

acgggcaaggccaagtgggatgctggaatgagctgaaagggacttccaaggaagatgccatgaaagcttacatcaacaaagtagaagagctaaagaaaaaatacggg

A list of the siRNAs appears in Table III (see Examples Section, ExampleII)

The set of duplexes was analyzed to identify correlations between siRNAfunctionality and other biophysical or thermodynamic properties. Whenthe siRNA panel was analyzed in functional and non-functional subgroups,certain nucleotides were much more abundant at certain positions infunctional or non-functional groups. More specifically, the frequency ofeach nucleotide at each position in highly functional siRNA duplexes wascompared with that of nonfunctional duplexes in order to assess thepreference for or against any given nucleotide at every position. Theseanalyses were used to determine important criteria to be included in thesiRNA algorithms (Formulas VIII and IX).

The data set was also analyzed for distinguishing biophysical propertiesof siRNAs in the functional group, such as optimal percent of GCcontent, propensity for internal structures and regional thermodynamicstability. Of the presented criteria, several are involved in duplexrecognition, RISC activation/duplex unwinding, and target cleavagecatalysis.

The original data set that was the source of the statistically derivedcriteria is shown in FIG. 2. Additionally, this figure shows that randomselection yields siRNA duplexes with unpredictable and widely varyingsilencing potencies as measured in tissue culture using HEK293 cells. Inthe figure, duplexes are plotted such that each x-axis tick-markrepresents an individual siRNA, with each subsequent siRNA differing intarget position by two nucleotides for Human Cyclophilin and FireflyLuciferase, and by one nucleotide for Human DBI. Furthermore, the y-axisdenotes the level of target expression remaining after transfection ofthe duplex into cells and subsequent silencing of the target.

SiRNA identified and optimized in this document work equally well in awide range of cell types. FIG. 3A shows the evaluation of thirty siRNAstargeting the DBI gene in three cell lines derived from differenttissues. Each DBI siRNA displays very similar functionality in HEK293(ATCC, CRL-1573, human embryonic kidney), HeLa (ATCC, CCL-2, cervicalepithelial adenocarcinoma) and DU145 (HTB-81, prostate) cells asdetermined by the B-DNA assay. Thus, siRNA functionality is determinedby the primary sequence of the siRNA and not by the intracellularenvironment. Additionally, it should be noted that although the presentinvention provides for a determination of the functionality of siRNA fora given target, the same siRNA may silence more than one gene. Forexample, the complementary sequence of the silencing siRNA may bepresent in more than one gene. Accordingly, in these circumstances, itmay be desirable not to use the siRNA with highest SMARTscore™. In suchcircumstances, it may be desirable to use the siRNA with the nexthighest SMARTscore™.

To determine the relevance of G/C content in siRNA function, the G/Ccontent of each duplex in the panel was calculated and the functionalclasses of siRNAs (<F50, ≧F50, ≧F80, ≧F95 where F refers to the percentgene silencing) were sorted accordingly. The majority of thehighly-functional siRNAs (≧F95) fell within the G/C content range of36%-52% (FIG. 3B). Twice as many non-functional (<F50) duplexes fellwithin the high G/C content groups (>57% GC content) compared to the36%-52% group. The group with extremely low GC content (26% or less)contained a higher proportion of non-functional siRNAs and nohighly-functional siRNAs. The G/C content range of 30%-52% was thereforeselected as Criterion I for siRNA functionality, consistent with theobservation that a G/C range 30%-70% promotes efficient RNAi targeting.Application of this criterion alone provided only a marginal increase inthe probability of selecting functional siRNAs from the panel: selectionof F50 and F95 siRNAs was improved by 3.6% and 2.2%, respectively. ThesiRNA panel presented here permitted a more systematic analysis andquantification of the importance of this criterion than that usedpreviously.

A relative measure of local internal stability is the A/U base pair (bp)content; therefore, the frequency of A/U bp was determined for each ofthe five terminal positions of the duplex (5′ sense (S)/5′ antisense(AS)) of all siRNAs in the panel. Duplexes were then categorized by thenumber of A/U bp in positions 1-5 and 15-19 of the sense strand. Thethermodynamic flexibility of the duplex 5′-end (positions 1-5; S) didnot appear to correlate appreciably with silencing potency, while thatof the 3′-end (positions 15-19; S) correlated with efficient silencing.No duplexes lacking A/U bp in positions 15-19 were functional. Thepresence of one A/U bp in this region conferred some degree offunctionality, but the presence of three or more A/Us was preferable andtherefore defined as Criterion II. When applied to the test panel, onlya marginal increase in the probability of functional siRNA selection wasachieved: a 1.8% and 2.3% increase for F50 and F95 duplexes,respectively (Table IV).

The complementary strands of siRNAs that contain internal repeats orpalindromes may form internal fold-back structures. These hairpin-likestructures exist in equilibrium with the duplexed form effectivelyreducing the concentration of functional duplexes. The propensity toform internal hairpins and their relative stability can be estimated bypredicted melting temperatures. High Tm reflects a tendency to formhairpin structures. Lower Tm values indicate a lesser tendency to formhairpins. When the functional classes of siRNAs were sorted by T_(m)(FIG. 3C), the following trends were identified: duplexes lacking stableinternal repeats were the most potent silencers (no F95 duplex withpredicted hairpin structure T_(m)>60° C.). In contrast, about 60% of theduplexes in the groups having internal hairpins with calculated T_(m)values less than 20° C. were F80. Thus, the stability of internalrepeats is inversely proportional to the silencing effect and definesCriterion III (predicted hairpin structure T_(m)≦20° C.).

Sequence-Based Determinants of siRNA Functionality

When the siRNA panel was sorted into functional and non-functionalgroups, the frequency of a specific nucleotide at each position in afunctional siRNA duplex was compared with that of a nonfunctional duplexin order to assess the preference for or against a certain nucleotide.FIG. 4 shows the results of these queries and the subsequent resortingof the data set (from FIG. 2). The data is separated into two sets:those duplexes that meet the criteria, a specific nucleotide in acertain position—grouped on the left (Selected) and those that donot—grouped on the right (Eliminated). The duplexes are further sortedfrom most functional to least functional with the y-axis of FIGS. 4A-Erepresenting the % expression i.e. the amount of silencing that iselicited by the duplex (Note: each position on the X-axis represents adifferent duplex). Statistical analysis revealed correlations betweensilencing and several sequence-related properties of siRNAs. FIGS. 4A-4Eand Table IV show quantitative analysis for the following fivesequence-related properties of siRNA: (4A) an A at position 19 of thesense strand; (4B) an A at position 3 of the sense strand; (4C) a U atposition 10 of the sense strand; (4D) a base other than G at position 13of the sense strand; and (4E) a base other than C at position 19 of thesense strand.

When the siRNAs in the panel were evaluated for the presence of an A atposition 19 of the sense strand, the percentage of non-functionalduplexes decreased from 20% to 11.8%, and the percentage of F95 duplexesincreased from 21.7% to 29.4% (Table IV). Thus, the presence of an A inthis position defined Criterion IV.

Another sequence-related property correlated with silencing was thepresence of an A in position 3 of the sense strand (FIG. 4B). Of thesiRNAs with A3, 34.4% were F95, compared with 21.7% randomly selectedsiRNAs. The presence of a U base in position 10 of the sense strandexhibited an even greater impact (FIG. 4C). Of the duplexes in thisgroup, 41.7% were F95. These properties became criteria V and VI,respectively.

Two negative sequence-related. criteria that were identified also appearon FIG. 4. The absence of a G at position 13 of the sense strand,conferred a marginal increase in selecting functional duplexes (FIG.4D). Similarly, lack of a C at position 19 of the sense strand alsocorrelated with functionality (FIG. 4E). Thus, among functionalduplexes, position 19 was most likely occupied by A, and rarely occupiedby C. These rules were defined as criteria VII and VIII, respectively.

Application of each criterion individually provided marginal butstatistically significant increases in the probability of selecting apotent siRNA. Although the results were informative, the inventorssought to maximize potency and therefore consider multiple criteria orparameters. Optimization is particularly important when developingtherapeutics. Interestingly, the probability of selecting a functionalsiRNA based on each thermodynamic criteria was 2%-4% higher than random,but 4%-8% higher for the sequence-related determinates. Presumably,these sequence-related increases reflect the complexity of the RNAimechanism and the multitude of protein-RNA interactions that areinvolved in RNAi-mediated silencing. TABLE IV Improvement Criterion %Functional over Random I. 30%-52% G/C content <F50 16.4% −3.6%  ≧F5083.6% 3.6% ≧F80 60.4% 4.3% ≧F95 23.9% 2.2% II. At least 3 A/U bases at<F50 18.2% −1.8%  positions 15-19 of the sense ≧F50 81.8% 1.8% strand≧F80 59.7% 3.6% ≧F95 24.0% 2.3% III. Absence of internal <F50 16.7%−3.3%  repeats, as measured by T_(m) of ≧F50 83.3% 3.3% secondarystructure ≦20° C. ≧F80 61.1% 5.0% ≧F95 24.6% 2.9% IV. An A base atposition 19 <F50 11.8% −8.2%  of the sense strand ≧F50 88.2% 8.2% ≧F8075.0% 18.9%  ≧F95 29.4% 7.7% V. An A base at position 3 of <F50 17.2%−2.8%  the sense strand ≧F50 82.8% 2.8% ≧F80 62.5% 6.4% ≧F95 34.4%12.7%  VI. A U base at position 10 <F50 13.9% −6.1%  of the sense strand≧F50 86.1% 6.1% ≧F80 69.4% 13.3%  ≧F95 41.7%  20% VII. A base other thanC at <F50 18.8% −1.2%  position 19 of the sense strand ≧F50 81.2% 1.2%≧F80 59.7% 3.6% ≧F95 24.2% 2.5% VIII. A base other than G at <F50 15.2%−4.8%  position 13 of the sense strand ≧F50 84.8% 4.8% ≧F80 61.4% 5.3%≧F95 26.5% 4.8%The siRNA Selection Algorithm

In an effort to improve selection further, all identified criteria,including but not limited to those listed in Table IV were combined intothe algorithms embodied in Formula VIII and Formula IX. Each siRNA wasthen assigned a score (referred to as a SMARTscore™) according to thevalues derived from the formulas. Duplexes that scored higher than 0 or20, for Formulas VIII and IX, respectively, effectively selected a setof functional siRNAs and excluded all non-functional siRNAs. Conversely,all duplexes scoring lower than 0 and 20 according to formulas VIII andIX, respectively, contained some functional siRNAs but included allnon-functional siRNAs. A graphical representation of this selection isshown in FIG. 5.

The methods for obtaining the seven criteria embodied in Table IV areillustrative of the results of the process used to develop theinformation for Formulas VIII and IX. Thus similar techniques were usedto establish the other variables and their multipliers. As describedabove, basic statistical methods were use to determine the relativevalues for these multipliers.

To determine the value for “Improvement over Random” the difference inthe frequency of a given attribute (e.g. GC content, base preference) ata particular position is determined between individual functional groups(e.g. <F50) and the total siRNA population studied (e.g. 270 siRNAmolecules selected randomly). Thus, for instance, in Criterion I(30%-52% GC content) members of the <F50 group were observed to have GCcontents between 30-52% in 16.4% of the cases. In contrast, the totalgroup of 270 siRNAs had GC contents in this range, 20% of the time. Thusfor this particular attribute, there is a small negative correlationbetween 30%-52% GC content and this functional group (i.e.16.4%−20%=−3.6%). Similarly, for Criterion VI, (a “U” at position 10 ofthe sense strand), the >F95 group contained a “U” at this position 41.7%of the time. In contrast, the total group of 270 siRNAs had a “U” atthis position 21.7% of the time, thus the improvement over random iscalculated to be 20% (or 41.7%-21.7%).

Identifying the Average Internal Stability Profile of Strong siRNA

In order to identify an internal stability profile that ischaracteristic of strong siRNA, 270 different siRNAs derived from thecyclophilin B, the diazepam binding inhibitor (DBI), and the luciferasegene were individually transfected into HEK293 cells and tested fortheir ability to induce RNAi of the respective gene. Based on theirperformance in the in vivo assay, the sequences were then subdividedinto three groups, (i) >95% silencing; (ii) 80-95% silencing; and (iii)less than 50% silencing. Sequences exhibiting 51-84% silencing wereeliminated from further consideration to reduce the difficulties inidentifying relevant thermodynamic patterns.

Following the division of siRNA into three groups, a statisticalanalysis was performed on each member of each group to determine theaverage internal stability profile (AISP) of the siRNA. To accomplishthis the Oligo 5.0 Primer Analysis Software and other relatedstatistical packages (e.g. Excel) were exploited to determine theinternal stability of pentamers using the nearest neighbor methoddescribed by Freier et al., (1986) Improved free-energy parameters forpredictions of RNA duplex stability, Proc Natl. Acad. Sci. U.S.A.83(24): 9373-7. Values for each group at each position were thenaveraged, and the resulting data were graphed on a linear coordinatesystem with the Y-axis expressing the ΔG (free energy) values inkcal/mole and the X-axis identifying the position of the base relativeto the 5′ end.

The results of the analysis identified multiple key regions in siRNAmolecules that were critical for successful gene silencing. At the3′-most end of the sense strand (5′antisense), highly functional siRNA(>95% gene silencing, see FIG. 6A, >F95) have a low internal stability(AISP of position 19=˜−7.6 kcal/mol). In contrast low-efficiency siRNA(i.e. those exhibiting less than 50% silencing, <F50) display adistinctly different profile, having high ΔG values (˜−8.4 kcal/mol) forthe same position. Moving in a 5′ (sense strand) direction, the internalstability of highly efficient siRNA rises (position 12=˜−8.3 kcal/mole)and then drops again (position 7=˜−7.7 kcal/mol) before leveling off ata value of approximately −8.1 kcal/mol for the 5′ terminus. SiRNA withpoor silencing capabilities show a distinctly different profile. Whilethe AISP value at position 12 is nearly identical with that of strongsiRNAs, the values at positions 7 and 8 rise considerably, peaking at ahigh of ˜−9.0 kcal/mol. In addition, at the 5′ end of the molecule theAISP profile of strong and weak siRNA differ dramatically. Unlike therelatively strong values exhibited by siRNA in the >95% silencing group,siRNAs that exhibit poor silencing activity have weak AISP values (−7.6,−7.5, and −7.5 kcal/mol for positions 1, 2 and 3 respectively).

Overall the profiles of both strong and weak siRNAs form distinctsinusoidal shapes that are roughly 180° out-of-phase with each other.While these thermodynamic descriptions define the archetypal profile ofa strong siRNA, it will likely be the case that neither the ΔG valuesgiven for key positions in the profile or the absolute position of theprofile along the Y-axis (i.e. the ΔG-axis) are absolutes. Profiles thatare shifted upward or downward (i.e. having on an average, higher orlower values at every position) but retain the relative shape andposition of the profile along the X-axis can be foreseen as beingequally effective as the model profile described here. Moreover, it islikely that siRNA that have strong or even stronger gene-specificsilencing effects might have exaggerated ΔG values (either higher orlower) at key positions. Thus, for instance, it is possible that the5′-most position of the sense strand (position 19) could have ΔG valuesof 7.4 kcal/mol or lower and still be a strong siRNA if, for instance, aG-C→G-T/U mismatch were substituted at position 19 and altered duplexstability. Similarly, position 12 and position 7 could have values above8.3 kcal/mol and below 7.7 kcal/mole, respectively, without abating thesilencing effectiveness of the molecule. Thus, for instance, at position12, a stabilizing chemical modification (e.g. a chemical modification ofthe 2′ position of the sugar backbone) could be added that increases theaverage internal stability at that position. Similarly, at position 7,mismatches similar to those described previously could be introducedthat would lower the ΔG values at that position.

Lastly, it is important to note that while functional and non-functionalsiRNA were originally defined as those molecules having specificsilencing properties, both broader or more limiting parameters can beused to define these molecules. As used herein, unless otherwisespecified, “non-functional siRNA” are defined as those siRNA that induceless than 50% (<50%) target silencing, “semi-functional siRNA” induce50-79% target silencing, “functional siRNA” are molecules that induce80-95% gene silencing, and “highly-functional siRNA” are molecules thatinduce great than 95% gene silencing. These definitions are not intendedto be rigid and can vary depending upon the design and needs of theapplication. For instance, it is possible that a researcher attemptingto map a gene to a chromosome using a functional assay, may identify ansiRNA that reduces gene activity by only 30%. While this level of genesilencing may be “non-functional” for e.g. therapeutic needs, it issufficient for gene mapping purposes and is, under these uses andconditions, “functional.” For these reasons, functional siRNA can bedefined as those molecules having greater than 10%, 20%, 30%, 40%, 50%,60%, 70%, 80%, or 90% silencing capabilities at 100 nM transfectionconditions. Similarly, depending upon the needs of the study and/orapplication, non-functional and semi-functional siRNA can be defined ashaving different parameters. For instance, semi-functional siRNA can bedefined as being those molecules that induce 20%, 30%, 40%, 50%, 60%, or70% silencing at 100 nM transfection conditions. Similarly,non-functional siRNA can be defined as being those molecules thatsilence gene expression by less than 70%, 60%, 50%, 40%, 30%, or less.Nonetheless, unless otherwise stated, the descriptions stated in the“Definitions” section of this text should be applied.

Functional attributes can be assigned to each of the key positions inthe AISP of strong siRNA. The low 5′ (sense strand) AISP values ofstrong siRNAs may be necessary for determining which end of the moleculeenters the RISC complex. In contrast, the high and low AISP valuesobserved in the central regions of the molecule may be critical forsiRNA-target mRNA interactions and product release, respectively.

If the AISP values described above accurately define the thermodynamicparameters of strong siRNA, it would be expected that similar patternswould be observed in strong siRNA isolated from nature. Natural siRNAsexist in a harsh, RNase-rich environment and it can be hypothesized thatonly those siRNA that exhibit heightened affinity for RISC (i.e. siRNAthat exhibit an average internal stability profile similar to thoseobserved in strong siRNA) would survive in an intracellular environment.This hypothesis was tested using GFP-specific siRNA isolated from N.benthamiana. Llave et al. (2002) Endogenous and Silencing-AssociatedSmall RNAs in Plants, The Plant Cell 14, 1605-1619, introduced longdouble-stranded GFP-encoding RNA into plants and subsequentlyre-isolated GFP-specific siRNA from the tissues. The AISP of fifty-nineof these GFP-siRNA were determined, averaged, and subsequently plottedalongside the AISP profile obtained from the cyclophilinB/DBI/luciferase siRNA having >90% silencing properties (FIG. 6B).Comparison of the two groups show that profiles are nearly identical.This finding validates the information provided by the internalstability profiles and demonstrates that: (1) the profile identified byanalysis of the cyclophilin B/DBI/luciferase siRNAs are not genespecific; and (2) AISP values can be used to search for strong siRNAs ina variety of species.

Both chemical modifications and base-pair mismatches can be incorporatedinto siRNA to alter the duplex's AISP and functionality. For instance,introduction of mismatches at positions 1 or 2 of the sense stranddestabilized the 5′ end of the sense strand and increases thefunctionality of the molecule (see Luc, FIG. 7). Similarly, addition of2′-O-methyl groups to positions 1 and 2 of the sense strand can alsoalter the AISP and (as a result) increase both the functionality of themolecule and eliminate off-target effects that results from sense strandhomology with the unrelated targets (FIG. 8).

Rationale for Criteria in a Biological Context

The fate of siRNA in the RNAi pathway may be described in 5 major steps:(1) duplex recognition and pre-RISC complex formation; (2) ATP-dependentduplex unwinding/strand selection and RISC activation; (3) mRNA targetidentification; (4) mRNA cleavage, and (5) product release (FIG. 1).Given the level of nucleic acid-protein interactions at each step, siRNAfunctionality is likely influenced by specific biophysical and molecularproperties that promote efficient interactions within the context of themulti-component complexes. Indeed, the systematic analysis of the siRNAtest set identified multiple factors that correlate well withfunctionality. When combined into a single algorithm, they proved to bevery effective in selecting active siRNAs.

The factors described here may also be predictive of key functionalassociations important for each step in RNAi. For example, the potentialformation of internal hairpin structures correlated negatively withsiRNA functionality. Complementary strands with stable internal repeatsare more likely to exist as stable hairpins thus decreasing theeffective concentration of the functional duplex form. This suggeststhat the duplex is the preferred conformation for initial pre-RISCassociation. Indeed, although single complementary strands can inducegene silencing, the effective concentration required is at least twoorders of magnitude higher than that of the duplex form.

siRNA-pre-RISC complex formation is followed by an ATP-dependent duplexunwinding step and “activation” of the RISC. The siRNA functionality wasshown to correlate with overall low internal stability of the duplex andlow internal stability of the 3′ sense end (or differential internalstability of the 3′ sense compare to the 5′ sense strand), which mayreflect strand selection and entry into the RISC. Overall duplexstability and low internal stability at the 3′ end of the sense strandwere also correlated with siRNA functionality. Interestingly, siRNAswith very high and very low overall stability profiles correlatestrongly with non-functional duplexes. One interpretation is that highinternal stability prevents efficient unwinding while very low stabilityreduces siRNA target affinity and subsequent mRNA cleavage by the RISC.

Several criteria describe base preferences at specific positions of thesense strand and are even more intriguing when considering theirpotential mechanistic roles in target recognition and mRNA cleavage.Base preferences for A at position 19 of the sense strand but not C, areparticularly interesting because they reflect the same base preferencesobserved for naturally occurring miRNA precursors. That is, among thereported miRNA precursor sequences 75% contain a U at position 1 whichcorresponds to an A in position 19 of the sense strand of siRNAs, whileG was under-represented in this same position for miRNA precursors.These observations support the hypothesis that both miRNA precursors andsiRNA duplexes are processed by very similar if not identical proteinmachinery. The functional interpretation of the predominance of a U/Abase pair is that it promotes flexibility at the 5′antisense ends ofboth siRNA duplexes and miRNA precursors and facilitates efficientunwinding and selective strand entrance into an activated RISC.

Among the criteria associated with base preferences that are likely toinfluence mRNA cleavage or possibly product release, the preference forU at position 10 of the sense strand exhibited the greatest impact,enhancing the probability of selecting an F80 sequence by 13.3%.Activated RISC preferentially cleaves target mRNA between nucleotides 10and 11 relative to the 5′ end of the complementary targeting strand.Therefore, it may be that U, the preferred base for mostendoribonucleases, at this position supports more efficient cleavage.Alternatively, a U/A bp between the targeting siRNA strand and itscognate target mRNA may create an optimal conformation for theRISC-associated “slicing” activity.

Pooling

According to another embodiment, the present invention provides a poolof at least two siRNAs, preferably in the form of a kit or therapeuticreagent, wherein one strand of each of the siRNAs, the sense strandcomprises a sequence that is substantially similar to a sequence withina target mRNA. The opposite strand, the antisense strand, willpreferably comprise a sequence that is substantially complementary tothat of the target mRNA. More preferably, one strand of each siRNA willcomprise a sequence that is identical to a sequence that is contained inthe target mRNA. Most preferably, each siRNA will be 19 base pairs inlength, and one strand of each of the siRNAs will be 100% complementaryto a portion of the target mRNA.

By increasing the number of siRNAs directed to a particular target usinga pool or kit, one is able both to increase the likelihood that at leastone siRNA with satisfactory functionality will be included, as well asto benefit from additive or synergistic effects. Further, when two ormore siRNAs directed against a single gene do not have satisfactorylevels of functionality alone, if combined, they may satisfactorilypromote degradation of the target messenger RNA and successfully inhibittranslation. By including multiple siRNAs in the system, not only is theprobability of silencing increased, but the economics of operation arealso improved when compared to adding different siRNAs sequentially.This effect is contrary to the conventional wisdom that the concurrentuse of multiple siRNA will negatively impact gene silencing (e.g. Holen,T. et al. (2003) “Similar behavior of single strand and double strandsiRNAs suggests they act through a common RNAi pathway.” NAR 31:2401-21407).

In fact, when two siRNAs were pooled together, 54% of the pools of twosiRNAs induced more than 95% gene silencing. Thus, a 2.5-fold increasein the percentage of functionality was achieved by randomly combiningtwo siRNAs. Further, over 84% of pools containing two siRNAs inducedmore than 80% gene silencing.

More preferably, the kit is comprised of at least three siRNAs, whereinone strand of each siRNA comprises a sequence that is substantiallysimilar to a sequence of the target mRNA and the other strand comprisesa sequence that is substantially complementary to the region of thetarget mRNA. As with the kit that comprises at least two siRNAs, morepreferably one strand will comprise a sequence that is identical to asequence that is contained in the mRNA and another strand that is 100%complementary to a sequence that is contained in the mRNA. Duringexperiments, when three siRNAs were combined together, 60% of the poolsinduced more than 95% gene silencing and 92% of the pools induced morethan 80% gene silencing.

Further, even more preferably, the kit is comprised of at least foursiRNAs, wherein one strand of each siRNA comprises a sequence that issubstantially similar to a region of the sequence of the target mRNA,and the other strand comprises a sequence that is substantiallycomplementary to the region of the target mRNA. As with the kit or poolthat comprises at least two siRNAs, more preferably one strand of eachof the siRNA duplexes will comprise a sequence that is identical to asequence that is contained in the mRNA, and another strand that is 100%complementary to a sequence that is contained in the mRNA.

Additionally, kits and pools with at least five, at least six, and atleast seven siRNAs may also be useful with the present invention. Forexample, pools of five siRNA induced 95% gene silencing with 77%probability and 80% silencing with 98.8% probability. Thus, pooling ofsiRNAs together can result in the creation of a target-specificsilencing reagent with almost a 99% probability of being functional. Thefact that such high levels of success are achievable using such pools ofsiRNA, enables one to dispense with costly and time-consumingtarget-specific validation procedures.

For this embodiment, as well as the other aforementioned embodiments,each of the siRNAs within a pool will preferably comprise between 18 and30 base pairs, more preferably between 18 and 25 base pairs, and mostpreferably 19 base pairs. Within each siRNA, preferably at least 18contiguous bases of the antisense strand will be 100% complementary tothe target mRNA. More preferably, at least 19 contiguous bases of theantisense strand will be 100% complementary to the target mRNA.Additionally, there may be overhangs on either the sense strand or theantisense strand, and these overhangs may be at either the 5′ end or the3′ end of either of the strands, for example there may be one or moreoverhangs of 1-6 bases. When overhangs are present, they are notincluded in the calculation of the number of base pairs. The twonucleotide 3′ overhangs mimic natural siRNAs and are commonly used butare not essential. Preferably, the overhangs should consist of twonucleotides, most often dTdT or UU at the 3′ end of the sense andantisense strand that are not complementary to the target sequence. ThesiRNAs may be produced by any method that is now known or that comes tobe known for synthesizing double stranded RNA that one skilled in theart would appreciate would be useful in the present invention.Preferably, the siRNAs will be produced by Dharmacon's proprietary ACE®technology. However, other methods for synthesizing siRNAs are wellknown to persons skilled in the art and include, but are not limited to,any chemical synthesis of RNA oligonucleotides, ligation of shorteroligonucleotides, in vitro transcription of RNA oligonucleotides, theuse of vectors for expression within cells, recombinant Dicer productsand PCR products.

The siRNA duplexes within the aforementioned pools of siRNAs maycorrespond to overlapping sequences within a particular mRNA, ornon-overlapping sequences of the mRNA. However, preferably theycorrespond to non-overlapping sequences. Further, each siRNA may beselected randomly, or one or more of the siRNA may be selected accordingto the criteria discussed above for maximizing the effectiveness ofsiRNA.

Included in the definition of siRNAs are siRNAs that contain substitutedand/or labeled nucleotides that may, for example, be labeled byradioactivity, fluorescence or mass. The most common substitutions areat the 2′ position of the ribose sugar, where moieties such as H(hydrogen) F, NH₃, OCH₃ and other O— alkyl, alkenyl, alkynyl, andorthoesters, may be substituted, or in the phosphorous backbone, wheresulfur, amines or hydrocarbons may be substituted for the bridging ofnon-bridging atoms in the phosphodiester bond. Examples of modifiedsiRNAs are explained more fully in commonly assigned U.S. patentapplication Ser. No. 10/613,077, filed Jul. 1, 2003, which isincorporated by reference herein.

Additionally, as noted above, the cell type into which the siRNA isintroduced may affect the ability of the siRNA to enter the cell;however, it does not appear to affect the ability of the siRNA tofunction once it enters the cell. Methods for introducingdouble-stranded RNA into various cell types are well known to personsskilled in the art.

As persons skilled in the art are aware, in certain species, thepresence of proteins such as RdRP, the RNA-dependent RNA polymerase, maycatalytically enhance the activity of the siRNA. For example, RdRPpropagates the RNAi effect in C. elegans and other non-mammalianorganisms. In fact, in organisms that contain these proteins, the siRNAmay be inherited. Two other proteins that are well studied and known tobe a part of the machinery are members of the Argonaute family andDicer, as well as their homologues. There is also initial evidence thatthe RISC complex might be associated with the ribosome so the moreefficiently translated mRNAs will be more susceptible to silencing thanothers.

Another very important factor in the efficacy of siRNA is mRNAlocalization. In general, only cytoplasmic mRNAs are considered to beaccessible to RNAi to any appreciable degree. However, appropriatelydesigned siRNAs, for example, siRNAs modified with internucleotidelinkages, may be able to cause silencing by acting in the nucleus.Examples of these types of modifications are described in commonlyassigned U.S. patent application Ser. Nos. 10/431,027 and 10/613,077,each of which is incorporated by reference herein.

As described above, even when one selects at least two siRNAs at random,the effectiveness of the two may be greater than one would predict basedon the effectiveness of two individual siRNAs. This additive orsynergistic effect is particularly noticeable as one increases to atleast three siRNAs, and even more noticeable as one moves to at leastfour siRNAs. Surprisingly, the pooling of the non-functional andsemi-functional siRNAs, particularly more than five siRNAs, can lead toa silencing mixture that is as effective if not more effective as anyone particular functional siRNA.

Within the kit of the present invention, preferably each siRNA will bepresent in a concentration of between 0.001 and 200 μM, more preferablybetween 0.01 and 200 nM, and most preferably between 0.1 and 10 nM.

In addition to preferably comprising at least four or five siRNAs, thekit of the present invention will also preferably comprise a buffer tokeep the siRNA duplex stable. Persons skilled in the art are aware ofbuffers suitable for keeping siRNA stable. For example, the buffer maybe comprised of 100 mM KCl, 30 mM HEPES-pH 7.5, and 1 mM MgCl₂.Alternatively, kits might contain complementary strands that contain anyone of a number of chemical modifications (e.g. a 2′-O-ACE) that protectthe agents from degradation by nucleases. In this instance, the user may(or may not) remove the modifying protective group (e.g. deprotect)before annealing the two complementary strands together.

By way of example, the kit may be organized such that pools of siRNAduplexes are provided on an array or microarray of wells or drops for aparticular gene set or for unrelated genes. The array may, for example,be in 96 wells, 384 wells or 1284 wells arrayed in a plastic plate or ona glass slide using techniques now known or that come to be known topersons skilled in the art. Within an array, preferably there will becontrols such as functional anti-lamin A/C, cyclophilin and two siRNAduplexes that are not specific to the gene of interest.

In order to ensure stability of the siRNA pools prior to usage, they maybe retained in lyophilized form at minus twenty degrees (−20° C.) untilthey are ready for use. Prior to usage, they should be resuspended;however, even once resuspended, for example, in the aforementionedbuffer, they should be kept at minus twenty degrees, (−20° C.) untilused. The aforementioned buffer, prior to use, may be stored atapproximately 4° C. or room temperature. Effective temperatures at whichto conduct transfections are well known to persons skilled in the artand include for example, room temperature.

The kit may be applied either in vivo or in vitro. Preferably, the siRNAof the pools or kits is applied to a cell through transfection,employing standard transfection protocols. These methods are well knownto persons skilled in the art and include the use of lipid-basedcarriers, electroporation, cationic carriers, and microinjection.Further, one could apply the present invention by synthesizingequivalent DNA sequences (either as two separate, complementary strands,or as hairpin molecules) instead of siRNA sequences and introducing theminto cells through vectors. Once in the cells, the cloned DNA could betranscribed, thereby forcing the cells to generate the siRNA. Examplesof vectors suitable for use with the present application include but arenot limited to the standard transient expression vectors, adenoviruses,retroviruses, lentivirus-based vectors, as well as other traditionalexpression vectors. Any vector that has an adequate siRNA expression andprocession module may be used. Furthermore, certain chemicalmodifications to siRNAs, including but not limited to conjugations toother molecules, may be used to facilitate delivery. For certainapplications it may be preferable to deliver molecules withouttransfection by simply formulating in a physiological acceptablesolution.

This embodiment may be used in connection with any of the aforementionedembodiments. Accordingly, the sequences within any pool may be selectedby rational design.

Multigene Silencing

In addition to developing kits that contain multiple siRNA directedagainst a single gene, another embodiment includes the use of multiplesiRNA targeting multiple genes. Multiple genes may be targeted throughthe use of high- or hyper-functional siRNA. High- or hyper-functionalsiRNA that exhibit increased potency, require lower concentrations toinduce desired phenotypic (and thus therapeutic) effects. Thiscircumvents RISC saturation. It therefore reasons that if lowerconcentrations of a single siRNA are needed for knockout or knockdownexpression of one gene, then the remaining (uncomplexed) RISC will befree and available to interact with siRNA directed against two, three,four, or more, genes. Thus in this embodiment, the authors describe theuse of highly functional or hyper-functional siRNA to knock out threeseparate genes. More preferably, such reagents could be combined toknockout four distinct genes. Even more preferably, highly functional orhyperfunctional siRNA could be used to knock out five distinct genes.Most preferably, siRNA of this type could be used to knockout orknockdown the expression of six or more genes.

Hyperfunctional siRNA

The term hyperfunctional siRNA (hf-siRNA) describes a subset of thesiRNA population that induces RNAi in cells at low- or sub-nanomolarconcentrations for extended periods of time. These traits, heightenedpotency and extended longevity of the RNAi phenotype, are highlyattractive from a therapeutic standpoint. Agents having higher potencyrequire lesser amounts of the molecule to achieve the desiredphysiological response, thus reducing the probability of side effectsdue to “off-target” interference. In addition to the potentialtherapeutic benefits associated with hyperfunctional siRNA, hf-siRNA arealso desirable from an economic position. Hyperfunctional siRNA may costless on a per-treatment basis, thus reducing the overall expenditures toboth the manufacturer and the consumer.

Identification of hyperfunctional siRNA involves multiple steps that aredesigned to examine an individual siRNA agent's concentration- and/orlongevity-profiles. In one non-limiting example, a population of siRNAdirected against a single gene are first analyzed using the previouslydescribed algorithm (Formula VIII). Individual siRNA are then introducedinto a test cell line and assessed for the ability to degrade the targetmRNA. It is important to note that when performing this step it is notnecessary to test all of the siRNA. Instead, it is sufficient to testonly those siRNA having the highest SMARTscores™ (i.e.SMARTscore™ >−10). Subsequently, the gene silencing data is plottedagainst the SMARTscores™ (see FIG. 9). SiRNA that (1) induce a highdegree of gene silencing (i.e. they induce greater than 80% geneknockdown) and (2) have superior SMARTscores™ (i.e. a SMARTscore™of >−10, suggesting a desirable average internal stability profile) areselected for further investigations designed to better understand themolecule's potency and longevity. In one, non-limiting study dedicatedto understanding a molecule's potency, an siRNA is introduced into one(or more) cell types in increasingly diminishing concentrations (e.g.3.0→0.3 nM). Subsequently, the level of gene silencing induced by eachconcentration is examined and siRNA that exhibit hyperfunctional potency(i.e. those that induce 80% silencing or greater at e.g. picomolarconcentrations) are identified. In a second study, the longevityprofiles of siRNA having high (>−10) SMARTscores™ and greater than 80%silencing are examined. In one non-limiting example of how this isachieved, siRNA are introduced into a test cell line and the levels ofRNAi are measured over an extended period of time (e.g. 24-168 hrs).SiRNAs that exhibit strong RNA interference patterns (i.e. >80%interference) for periods of time greater than, e.g., 120 hours are thusidentified. Studies similar to those described above can be performed onany and all of the >10⁶ siRNA included in this document to furtherdefine the most functional molecule for any given gene. Moleculespossessing one or both properties (extended longevity and heightenedpotency) are labeled “hyperfunctional siRNA,” and earmarked ascandidates for future therapeutic studies.

While the example(s) given above describe one means by whichhyperfunctional siRNA can be isolated, neither the assays themselves northe selection parameters used are rigid and can vary with each family ofsiRNA. Families of siRNA include siRNAs directed against a single gene,or directed against a related family of genes.

The highest quality siRNA achievable for any given gene may varyconsiderably. Thus, for example, in the case of one gene (gene X),rigorous studies such as those described above may enable theidentification of an siRNA that, at picomolar concentrations, induces99⁺% silencing for a period of 10 days. Yet identical studies of asecond gene (gene Y) may yield an siRNA that at high nanomolarconcentrations (e.g. 100 nM) induces only 75% silencing for a period of2 days. Both molecules represent the very optimum siRNA for theirrespective gene targets and therefore are designated “hyperfunctional.”Yet due to a variety of factors including but not limited to targetconcentration, siRNA stability, cell type, off-target interference, andothers, equivalent levels of potency and longevity are not achievable.Thus, for these reasons, the parameters described in the beforementioned assays, can vary. While the initial screen selected siRNA thathad SMARTscores™ above −10 and a gene silencing capability of greaterthan 80%, selections that have stronger (or weaker) parameters can beimplemented. Similarly, in the subsequent studies designed to identifymolecules with high potency and longevity, the desired cutoff criteria(i.e. the lowest concentration that induces a desirable level ofinterference, or the longest period of time that interference can beobserved) can vary. The experimentation subsequent to application of therational criteria of this application is significantly reduced where oneis trying to obtain a suitable hyperfunctional siRNA for, for example,therapeutic use. When, for example, the additional experimentation ofthe type described herein is applied by one skilled in the art with thisdisclosure in hand, a hyperfunctional siRNA is readily identified.

The siRNA may be introduced into a cell by any method that is now knownor that comes to be known and that from reading this disclosure, personsskilled in the art would determine would be useful in connection withthe present invention in enabling siRNA to cross the cellular membrane.These methods include, but are not limited to, any manner oftransfection, such as for example transfection employing DEAE-Dextran,calcium phosphate, cationic lipids/liposomes, micelles, manipulation ofpressure, microinjection, electroporation, immunoporation, use ofvectors such as viruses, plasmids, cosmids, bacteriophages, cellfusions, and coupling of the polynucleotides to specific conjugates orligands such as antibodies, antigens, or receptors, passiveintroduction, adding moieties to the siRNA that facilitate its uptake,and the like.

Having described the invention with a degree of particularity, exampleswill now be provided. These examples are not intended to and should notbe construed to limit the scope of the claims in any way.

EXAMPLES General Techniques and Nomenclatures

siRNA nomenclature. All siRNA duplexes are referred to by sense strand.The first nucleotide of the 5′-end of the sense strand is position 1,which corresponds to position 19 of the antisense strand for a 19-mer.In most cases, to compare results from different experiments, silencingwas determined by measuring specific transcript mRNA levels or enzymaticactivity associated with specific transcript levels, 24 hourspost-transfection, with siRNA concentrations held constant at 100 nM.For all experiments, unless otherwise specified transfection efficiencywas ensured to be over 95%, and no detectable cellular toxicity wasobserved. The following system of nomenclature was used to compare andreport siRNA-silencing functionality: “F” followed by the degree ofminimal knockdown. For example, F50 signifies at least 50% knockdown,F80 means at least 80%, and so forth. For this study, all sub-F50 siRNAswere considered non-functional.

Cell culture and transfection. 96-well plates are coated with 50 μl of50 mg/ml poly-L-lysine (Sigma) for 1 hr, and then washed 3× withdistilled water before being dried for 20 min. HEK293 cells orHEK293Lucs or any other cell type of interest are released from theirsolid support by trypsinization, diluted to 3.5×10⁵ cells/ml, followedby the addition of 100 μL of cells/well. Plates are then incubatedovernight at 37° C., 5% CO₂. Transfection procedures can vary widelydepending on the cell type and transfection reagents. In onenon-limiting example, a transfection mixture consisting of 2 mL Opti-MEMI (Gibco-BRL), 80 μl Lipofectamine 2000 (Invitrogen), 15 μL SUPERNasinat 20 U/μl (Ambion), and 1.5 μl of reporter gene plasmid at 1 μg/μl isprepared in 5-ml polystyrene round bottom tubes. 100 μl of transfectionreagent is then combined with 100 μl of siRNAs in polystyrene deep-welltiter plates (Beckman) and incubated for 20 to 30 min at room temp. 550μl of Opti-MEM is then added to each well to bring the final siRNAconcentration to 100 nM. Plates are then sealed with parafilm and mixed.Media is removed from HEK293 cells and replaced with 95 μl oftransfection mixture. Cells are incubated overnight at 37° C., 5% CO₂.

Quantification of gene knockdown. A variety of quantification procedurescan be used to measure the level of silencing induced by siRNA or siRNApools. In one non-limiting example: to measure mRNA levels 24 hrspost-transfection, QuantiGene branched-DNA (bDNA) kits (Bayer) (Wang, etal, Regulation of insulin preRNA splicing by glucose. Proc Natl Acad Sci1997, 94:4360) are used according to manufacturer instructions. Tomeasure luciferase activity, media is removed from HEK293 cells 24 hrspost-transfection, and 50 μl of Steady-GLO reagent (Promega) is added.After 5 min, plates are analyzed on a plate reader.

Example I Sequences Used to Develop the Algorithm

Anti-Firefly and anti-Cyclophilin siRNAs panels (FIGS. 5A, B) sortedaccording to using Formula VIII predicted values. All siRNAs scoringmore than 0 (formula VIII) and more then 20 (formula IX) are fullyfunctional. All ninety sequences for each gene (and DBI) appear below inTable III. TABLE III Cyclo 1 SEQ. ID 0032 GUUCCAAAAACAGUGGAUA Cyclo 2SEQ. ID 0033 UCCAAAAACAGUGGAUAAU Cyclo 3 SEQ. ID 0034CAAAAACAGUGGAUAAUUU Cyclo 4 SEQ. ID 0035 AAAACAGUGGAUAAUUUUG Cyclo 5SEQ. ID 0036 AACAGUGGAUAAUUUUGUG Cyclo 6 SEQ. ID 0037CAGUGGAUAAUUUUGUGGC Cyclo 7 SEQ. ID 0038 GUGGAUAAUUUUGUGGCCU Cyclo 8SEQ. ID 0039 GGAUAAUUUUGUGGCCUUA Cyclo 9 SEQ. ID 0040AUAAUUUUGUGGCCUUAGC Cyclo 10 SEQ. ID 0041 AAUUUUGUGGCCUUAGCUA Cyclo 11SEQ. ID 0042 UUUUGUGGCCUUAGCUACA Cyclo 12 SEQ. ID 0043UUGUGGCCUUAGCUACAGG Cyclo 13 SEQ. ID 0044 GUGGCCUUAGCUACAGGAG Cyclo 14SEQ. ID 0045 GGCCUUAGCUACAGGAGAG Cyclo 15 SEQ. ID 0046CCUUAGCUACAGGAGAGAA Cyclo 16 SEQ. ID 0047 UUAGCUACAGGAGAGAAAG Cyclo 17SEQ. ID 0048 AGCUACAGGAGAGAAAGGA Cyclo 18 SEQ. ID 0049CUACAGGAGAGAAAGGAUU Cyclo 19 SEQ. ID 0050 ACAGGAGAGAAAGGAUUUG Cyclo 20SEQ. ID 0051 AGGAGAGAAAGGAUUUGGC Cyclo 21 SEQ. ID 0052GAGAGAAAGGAUUUGGCUA Cyclo 22 SEQ. ID 0053 GAGAAAGGAUUUGGCUACA Cyclo 23SEQ. ID 0054 GAAAGGAUUUGGCUACAAA Cyclo 24 SEQ. ID 0055AAGGAUUUGGCUACAAAAA Cyclo 25 SEQ. ID 0056 GGAUUUGGCUACAAAAACA Cyclo 26SEQ. ID 0057 AUUUGGCUACAAAAACAGC Cyclo 27 SEQ. ID 0058UUGGCUACAAAAACAGCAA Cyclo 28 SEQ. ID 0059 GGCUACAAAAACAGCAAAU Cyclo 29SEQ. ID 0060 CUACAAAAACAGCAAAUUC Cyclo 30 SEQ. ID 0061ACAAAAACAGCAAAUUCCA Cyclo 31 SEQ. ID 0062 AAAAACAGCAAAUUCCAUC Cyclo 32SEQ. ID 0063 AAACAGCAAAUUCCAUCGU Cyclo 33 SEQ. ID 0064ACAGCAAAUUCCAUCGUGU Cyclo 34 SEQ. ID 0065 AGCAAAUUCCAUCGUGUAA Cyclo 35SEQ. ID 0066 CAAAUUCCAUCGUGUAAUC Cyclo 36 SEQ. ID 0067AAUUCCAUCGUGUAAUCAA Cyclo 37 SEQ. ID 0068 UUCCAUCGUGUAAUCAAGG Cyclo 38SEQ. ID 0069 CCAUCGUGUAAUCAAGGAC Cyclo 39 SEQ. ID 0070AUCGUGUAAUCAAGGACUU Cyclo 40 SEQ. ID 0071 CGUGUAAUCAAGGACUUCA Cyclo 41SEQ. ID 0072 UGUAAUCAAGGACUUCAUG Cyclo 42 SEQ. ID 0073UAAUCAAGGACUUCAUGAU Cyclo 43 SEQ. ID 0074 AUCAAGGACUUCAUGAUCC Cyclo 44SEQ. ID 0075 CAAGGACUUCAUGAUCCAG Cyclo 45 SEQ. ID 0076AGGACUUCAUGAUCCAGGG Cyclo 46 SEQ. ID 0077 GACUUCAUGAUCCAGGGCG Cyclo 47SEQ. ID 0078 CUUCAUGAUCCAGGGCGGA Cyclo 48 SEQ. ID 0079UCAUGAUCCAGGGCGGAGA Cyclo 49 SEQ. ID 0080 AUGAUCCAGGGCGGAGACU Cyclo 50SEQ. ID 0081 GAUCCAGGGCGGAGACUUC Cyclo 51 SEQ. ID 0082UCCAGGGCGGAGACUUCAC Cyclo 52 SEQ. ID 0083 CAGGGCGGAGACUUCACCA Cyclo 53SEQ. ID 0084 GGGCGGAGACUUCACCAGG Cyclo 54 SEQ. ID 0085GCGGAGACUUCACCAGGGG Cyclo 55 SEQ. ID 0086 GGAGACUUCACCAGGGGAG Cyclo 56SEQ. ID 0087 AGACUUCACCAGGGGAGAU Cyclo 57 SEQ. ID 0088ACUUCACCAGGGGAGAUGG Cyclo 58 SEQ. ID 0089 UUCACCAGGGGAGAUGGCA Cyclo 59SEQ. ID 0090 CACCAGGGGAGAUGGCACA Cyclo 60 SEQ. ID 0091CCAGGGGAGAUGGCACAGG Cyclo 61 SEQ. ID 0092 AGGGGAGAUGGCACAGGAG Cyclo 62SEQ. ID 0093 GGGAGAUGGCACAGGAGGA Cyclo 63 SEQ. ID 0437GAGAUGGCACAGGAGGAAA Cyclo 64 SEQ. ID 0095 GAUGGCACAGGAGGAAAGA Cyclo 65SEQ. ID 0094 UGGCACAGGAGGAAAGAGC Cyclo 66 SEQ. ID 0096GCACAGGAGGAAAGAGCAU Cyclo 67 SEQ. ID 0097 ACAGGAGGAAAGAGCAUCU Cyclo 68SEQ. ID 0098 AGGAGGAAAGAGCAUCUAC Cyclo 69 SEQ. ID 0099GAGGAAAGAGCAUCUACGG Cyclo 70 SEQ. ID 0100 GGAAAGAGCAUCUACGGUG Cyclo 71SEQ. ID 0101 AAAGAGCAUCUACGGUGAG Cyclo 72 SEQ. ID 0102AGAGCAUCUACGGUGAGCG Cyclo 73 SEQ. ID 0103 AGCAUCUACGGUGAGCGCU Cyclo 74SEQ. ID 0104 CAUCUACGGUGAGCGCUUC Cyclo 75 SEQ. ID 0105UCUACGGUGAGCGCUUCCC Cyclo 76 SEQ. ID 0106 UACGGUGAGCGCUUCCCCG Cyclo 77SEQ. ID 0107 CGGUGAGCGCUUCCCCGAU Cyclo 78 SEQ. ID 0108GUGAGCGCUUCCCCGAUGA Cyclo 79 SEQ. ID 0109 GAGCGCUUCCCCGAUGAGA Cyclo 80SEQ. ID 0110 GCGCUUCCCCGAUGAGAAC Cyclo 81 SEQ. ID 0111GCUUCCCCGAUGAGAACUU Cyclo 82 SEQ. ID 0112 UUCCCCGAUGAGAACUUCA Cyclo 83SEQ. ID 0113 CCCCGAUGAGAACUUCAAA Cyclo 84 SEQ. ID 0114CCGAUGAGAACUUCAAACU Cyclo 85 SEQ. ID 0115 GAUGAGAACUUCAAACUGA Cyclo 86SEQ. ID 0116 UGAGAACUUCAAACUGAAG Cyclo 87 SEQ. ID 0117AGAACUUCAAACUGAAGCA Cyclo 88 SEQ. ID 0118 AACUUCAAACUGAAGCACU Cyclo 89SEQ. ID 0119 CUUCAAACUGAAGCACUAC Cyclo 90 SEQ. ID 0120UCAAACUGAAGCACUACGG DB 1 SEQ. ID 0121 ACGGGCAAGGCCAAGUGGG DB 2 SEQ. ID0122 CGGGCAAGGCCAAGUGGGA DB 3 SEQ. ID 0123 GGGCAAGGCCAAGUGGGAU DB 4 SEQ.ID 0124 GGCAAGGCCAAGUGGGAUG DB 5 SEQ. ID 0125 GCAAGGCCAAGUGGGAUGC DB 6SEQ. ID 0126 CAAGGCCAAGUGGGAUGCC DB 7 SEQ. ID 0127 AAGGCCAAGUGGGAUGCCUDB 8 SEQ. ID 0128 AGGCCAAGUGGGAUGCCUG DB 9 SEQ. ID 0129GGCCAAGUGGGAUGCCUGG DB 10 SEQ. ID 0130 GCCAAGUGGGAUGCCUGGA DB 11 SEQ. ID0131 CCAAGUGGGAUGCCUGGAA DB 12 SEQ. ID 0132 CAAGUGGGAUGCCUGGAAU DB 13SEQ. ID 0133 AAGUGGGAUGCCUGGAAUG DB 14 SEQ. ID 0134 AGUGGGAUGCCUGGAAUGADB 15 SEQ. ID 0135 GUGGGAUGCCUGGAAUGAG DB 16 SEQ. ID 0136UGGGAUGCCUGGAAUGAGC DB 17 SEQ. ID 0137 GGGAUGCCUGGAAUGAGCU DB 18 SEQ. ID0138 GGAUGCCUGGAAUGAGCUG DB 19 SEQ. ID 0139 GAUGCCUGGAAUGAGCUGA DB 20SEQ. ID 0140 AUGCCUGGAAUGAGCUGAA DB 21 SEQ. ID 0141 UGCCUGGAAUGAGCUGAAADB 22 SEQ. ID 0142 GCCUGGAAUGAGCUGAAAG DB 23 SEQ. ID 0143CCUGGAAUGAGCUGAAAGG DB 24 SEQ. ID 0144 CUGGAAUGAGCUGAAAGGG DB 25 SEQ. ID0145 UGGAAUGAGCUGAAAGGGA DB 26 SEQ. ID 0146 GGAAUGAGCUGAAAGGGAC DB 27SEQ. ID 0147 GAAUGAGCUGAAAGGGACU DB 28 SEQ. ID 0148 AAUGAGCUGAAAGGGACUUDB 29 SEQ. ID 0149 AUGAGCUGAAAGGGACUUC DB 30 SEQ. ID 0150UGAGCUGAAAGGGACUUCC DB 31 SEQ. ID 0151 GAGCUGAAAGGGACUUCCA DB 32 SEQ. ID0152 AGCUGAAAGGGACUUCCAA DB 33 SEQ. ID 0153 GCUGAAAGGGACUUCCAAG DB 34SEQ. ID 0154 CUGAAAGGGACUUCCAAGG DB 35 SEQ. ID 0155 UGAAAGGGACUUCCAAGGADB 36 SEQ. ID 0156 GAAAGGGACUUCCAAGGAA DB 37 SEQ. ID 0157AAAGGGACUUCCAAGGAAG DB 38 SEQ. ID 0158 AAGGGACUUCCAAGGAAGA DB 39 SEQ. ID0159 AGGGACUUCCAAGGAAGAU DB 40 SEQ. ID 0160 GGGACUUCCAAGGAAGAUG DB 41SEQ. ID 0161 GGACUUCCAAGGAAGAUGC DB 42 SEQ. ID 0162 GACUUCCAAGGAAGAUGCCDB 43 SEQ. ID 0163 ACUUCCAAGGAAGAUGCCA DB 44 SEQ. ID 0164CUUCCAAGGAAGAUGCCAU DB 45 SEQ. ID 0165 UUCCAAGGAAGAUGCCAUG DB 46 SEQ. ID0166 UCCAAGGAAGAUGCCAUGA DB 47 SEQ. ID 0167 CCAAGGAAGAUGCCAUGAA DB 48SEQ. ID 0168 CAAGGAAGAUGCCAUGAAA DB 49 SEQ. ID 0169 AAGGAAGAUGCCAUGAAAGDB 50 SEQ. ID 0170 AGGAAGAUGCCAUGAAAGC DB 51 SEQ. ID 0171GGAAGAUGCCAUGAAAGCU DB 52 SEQ. ID 0172 GAAGAUGCCAUGAAAGCUU DB 53 SEQ. ID0173 AAGAUGCCAUGAAAGCUUA DB 54 SEQ. ID 0174 AGAUGCCAUGAAAGCUUAC DB 55SEQ. ID 0175 GAUGCCAUGAAAGCUUACA DB 56 SEQ. ID 0176 AUGCCAUGAAAGCUUACAUDB 57 SEQ. ID 0177 UGGCAUGAAAGCUUACAUC DB 58 SEQ. ID 0178GCCAUGAAAGCUUACAUCA DB 59 SEQ. ID 0179 CCAUGAAAGCUUACAUCAA DB 60 SEQ. ID0180 CAUGAAAGCUUACAUCAAC DB 61 SEQ. ID 0181 AUGAAAGCUUACAUCAACA DB 62SEQ. ID 0182 UGAAAGCUUACAUCAACAA DB 63 SEQ. ID 0183 GAAAGCUUACAUCAACAAADB 64 SEQ. ID 0184 AAAGCUUACAUCAACAAAG DB 65 SEQ. ID 0185AAGCUUACAUCAACAAAGU DB 66 SEQ. ID 0186 AGCUUACAUCAACAAAGUA DB 67 SEQ. ID0187 GCUUACAUCAACAAAGUAG DB 68 SEQ. ID 0188 CUUACAUCAACAAAGUAGA DB 69SEQ. ID 0189 UUACAUCAACAAAGUAGAA DB 70 SEQ. ID 0190 UACAUCAACAAAGUAGAAGDB 71 SEQ. ID 0191 ACAUCAACAAAGUAGAAGA DB 72 SEQ. ID 0192CAUCAACAAAGUAGAAGAG DB 73 SEQ. ID 0193 AUCAACAAAGUAGAAGAGC DB 74 SEQ. ID0194 UCAACAAAGUAGAAGAGCU DB 75 SEQ. ID 0195 CAACAAAGUAGAAGAGCUA DB 76SEQ. ID 0196 AACAAAGUAGAAGAGCUAA DB 77 SEQ. ID 0197 ACAAAGUAGAAGAGCUAAADB 78 SEQ. ID 0198 CAAAGUAGAAGAGCUAAAG DB 79 SEQ. ID 0199AAAGUAGAAGAGCUAAAGA DB 80 SEQ. ID 0200 AAGUAGAAGAGCUAAAGAA DB 81 SEQ. ID0201 AGUAGAAGAGCUAAAGAAA DB 82 SEQ. ID 0202 GUAGAAGAGCUAAAGAAAA DB 83SEQ. ID 0203 UAGAAGAGCUAAAGAAAAA DB 84 SEQ. ID 0204 AGAAGAGCUAAAGAAAAAADB 85 SEQ. ID 0205 GAAGAGCUAAAGAAAAAAU DB 86 SEQ. ID 0206AAGAGCUAAAGAAAAAAUA DB 87 SEQ. ID 0207 AGAGCUAAAGAAAAAAUAC DB 88 SEQ. ID0208 GAGCUAAAGAAAAAAUACG DB 89 SEQ. ID 0209 AGCUAAAGAAAAAAUACGG DB 90SEQ. ID 0210 GCUAAAGAAAAAAUACGGG Luc 1 SEQ. ID 0211 AUCCUCAUAAAGGCCAAGALuc 2 SEQ. ID 0212 AGAUCCUCAUAAAGGCCAA Luc 3 SEQ. ID 0213AGAGAUCCUCAUAAAGGCC Luc 4 SEQ. ID 0214 AGAGAGAUCCUCAUAAAGG Luc 5 SEQ. ID0215 UCAGAGAGAUCCUCAUAAA Luc 6 SEQ. ID 0216 AAUCAGAGAGAUCCUCAUA Luc 7SEQ. ID 0217 AAAAUCAGAGAGAUCCUCA Luc 8 SEQ. ID 0218 GAAAAAUCAGAGAGAUCCULuc 9 SEQ. ID 0219 AAGAAAAAUCAGAGAGAUC Luc 10 SEQ. ID 0220GCAAGAAAAAUCAGAGAGA Luc 11 SEQ. ID 0221 ACGCAAGAAAAAUCAGAGA Luc 12 SEQ.ID 0222 CGACGCAAGAAAAAUCAGA Luc 13 SEQ. ID 0223 CUCGACGCAAGAAAAAUCA Luc14 SEQ. ID 0224 AACUCGACGCAAGAAAAAU Luc 15 SEQ. ID 0225AAAACUCGACGCAAGAAAA Luc 16 SEQ. ID 0226 GGAAAACUCGACGCAAGAA Luc 17 SEQ.ID 0227 CCGGAAAACUCGACGCAAG Luc 18 SEQ. ID 0228 UACCGGAAAACUCGACGCA Luc19 SEQ. ID 0229 CUUACCGGAAAACUCGACG Luc 20 SEQ. ID 0230GUCUUACCGGAAAACUCGA Luc 21 SEQ. ID 0231 AGGUCUUACCGGAAAACUC Luc 22 SEQ.ID 0232 AAAGGUCUUACCGGAAAAC Luc 23 SEQ. ID 0233 CGAAAGGUCUUACCGGAAA Luc24 SEQ. ID 0234 ACCGAAAGGUCUUACCGGA Luc 25 SEQ. ID 0235GUACCGAAAGGUCUUACCG Luc 26 SEQ. ID 0236 AAGUACCGAAAGGUCUUAC Luc 27 SEQ.ID 0237 CGAAGUACCGAAAGGUCUU Luc 28 SEQ. ID 0238 GACGAAGUACCGAAAGGUC Luc29 SEQ. ID 0239 UGGACGAAGUACCGAAAGG Luc 30 SEQ. ID 0240UGUGGACGAAGUACCGAAA Luc 31 SEQ. ID 0241 UUUGUGGACGAAGUACCGA Luc 32 SEQ.ID 0242 UGUUUGUGGACGAAGUACC Luc 33 SEQ. ID 0243 UGUGUUUGUGGACGAAGUA Luc34 SEQ. ID 0244 GUUGUGUUUGUGGACGAAG Luc 35 SEQ. ID 0245GAGUUGUGUUUGUGGACGA Luc 36 SEQ. ID 0246 AGGAGUUGUGUUUGUGGAC Luc 37 SEQ.ID 0247 GGAGGAGUUGUGUUUGUGG Luc 38 SEQ. ID 0248 GCGGAGGAGUUGUGUUUGU Luc39 SEQ. ID 0249 GCGCGGAGGAGUUGUGUUU Luc 40 SEQ. ID 0250UUGCGCGGAGGAGUUGUGU Luc 41 SEQ. ID 0251 AGUUGCGCGGAGGAGUUGU Luc 42 SEQ.ID 0252 AAAGUUGCGCGGAGGAGUU Luc 43 SEQ. ID 0253 AAAAAGUUGCGCGGAGGAG Luc44 SEQ. ID 0254 CGAAAAAGUUGCGCGGAGG Luc 45 SEQ. ID 0255CGCGAAAAAGUUGCGCGGA Luc 46 SEQ. ID 0256 ACCGCGAAAAAGUUGCGCG Luc 47 SEQ.ID 0257 CAACCGCGAAAAAGUUGCG Luc 48 SEQ. ID 0258 AACAACCGCGAAAAAGUUG Luc49 SEQ. ID 0259 GUAACAACCGCGAAAAAGU Luc 50 SEQ. ID 0260AAGUAACAACCGCGAAAAA Luc 51 SEQ. ID 0261 UCAAGUAACAACCGCGAAA Luc 52 SEQ.ID 0262 AGUCAAGUAACAACCGCGA Luc 53 SEQ. ID 0263 CCAGUCAAGUAACAACCGC Luc54 SEQ. ID 0264 CGCCAGUCAAGUAACAACC Luc 55 SEQ. ID 0265GUCGCCAGUCAAGUAACAA Luc 56 SEQ. ID 0266 ACGUCGCCAGUCAAGUAAC Luc 57 SEQ.ID 0267 UUACGUCGCCAGUCAAGUA Luc 58 SEQ. ID 0268 GAUUACGUCGCCAGUCAAG Luc59 SEQ. ID 0269 UGGAUUACGUCGCCAGUCA Luc 60 SEQ. ID 0270CGUGGAUUACGUCGCCAGU Luc 61 SEQ. ID 0271 AUCGUGGAUUACGUCGCCA Luc 62 SEQ.ID 0272 AGAUCGUGGAUUACGUCGC Luc 63 SEQ. ID 0273 AGAGAUCGUGGAUUACGUC Luc64 SEQ. ID 0274 AAAGAGAUCGUGGAUUACG Luc 65 SEQ. ID 0275AAAAAGAGAUCGUGGAUUA Luc 66 SEQ. ID 0276 GGAAAAAGAGAUCGUGGAU Luc 67 SEQ.ID 0277 ACGGAAAAAGAGAUCGUGG Luc 68 SEQ. ID 0278 UGACGGAAAAAGAGAUCGU Luc69 SEQ. ID 0279 GAUGACGGAAAAAGAGAUC Luc 70 SEQ. ID 0280ACGAUGACGGAAAAAGAGA Luc 71 SEQ. ID 0281 AGACGAUGACGGAAAAAGA Luc 72 SEQ.ID 0282 AAAGACGAUGACGGAAAAA Luc 73 SEQ. ID 0283 GGAAAGACGAUGACGGAAA Luc74 SEQ. ID 0284 ACGGAAAGACGAUGACGGA Luc 75 SEQ. ID 0285GCACGGAAAGACGAUGACG Luc 76 SEQ. ID 0286 GAGCACGGAAAGACGAUGA Luc 77 SEQ.ID 0287 UGGAGCACGGAAAGACGAU Luc 78 SEQ. ID 0288 UUUGGAGCACGGAAAGACG Luc79 SEQ. ID 0289 GUUUUGGAGCACGGAAAGA Luc 80 SEQ. ID 0290UUGUUUUGGAGCACGGAAA Luc 81 SEQ. ID 0291 UGUUGUUUUGGAGCACGGA Luc 82 SEQ.ID 0292 GUUGUUGUUUUGGAGCACG Luc 83 SEQ. ID 0293 CCGUUGUUGUUUUGGAGCA Luc84 SEQ. ID 0294 CGCCGUUGUUGUUUUGGAG Luc 85 SEQ. ID 0295GCCGCCGUUGUUGUUUUGG Luc 86 SEQ. ID 0296 CCGCCGCCGUUGUUGUUUU Luc 87 SEQ.ID 0297 UCCCGCCGCCGUUGUUGUU Luc 88 SEQ. ID 0298 CUUCCCGCCGCCGUUGUUG Luc89 SEQ. ID 0299 AACUUCCCGCCGCCGUUGU Luc 90 SEQ. ID 0300UGAACUUCCCGCCGCCGUU

Example II Validation of the Algorithm using DBI, Luciferase, PLK, EGFR,and SEAP

The algorithm (Formula VIII) identified siRNAs for five genes, humanDBI, firefly luciferase (fLuc), renilla luciferase (rLuc), human PLK,and human secreted alkaline phosphatase (SEAP). Four individual siRNAswere selected on the basis of their SMARTscores™ derived by analysis oftheir sequence using Formula VIII (all of the siRNAs would be selectedwith Formula IX as well) and analyzed for their ability to silence theirtargets' expression. In addition to the scoring, a BLAST search wasconducted for each siRNA. To minimize the potential for off-targetsilencing effects, only those target sequences with more than threemismatches against un-related sequences were selected. Semizarov, et al,Specificity of short interfering RNA determined through gene expressionsignatures. Proc. Natl. Acad. Sci. U.S.A. 2003, 100:6347. These duplexeswere analyzed individually and in pools of 4 and compared with severalsiRNAs that were randomly selected. The functionality was measured apercentage of targeted gene knockdown as compared to controls. AllsiRNAs were transfected as described by the methods above at 100 nMconcentration into HEK293 using Lipofectamine 2000. The level of thetargeted gene expression was evaluated by B-DNA as described above andnormalized to the non-specific control. FIG. 10 shows that the siRNAsselected by the algorithm disclosed herein were significantly morepotent than randomly selected siRNAs. The algorithm increased thechances of identifying an F50 siRNA from 48% to 91%, and an F80 siRNAfrom 13% to 57%. In addition, pools of SMART siRNA silence the selectedtarget better than randomly selected pools (see FIG. 10F).

Example III Validation of the Algorithm Using Genes Involved inClathrin-Dependent Endocytosis

Components of clathrin-mediated endocytosis pathway are key tomodulating intracellular signaling and play important roles in disease.Chromosomal rearrangements that result in fusion transcripts between theMixed-Lineage Leukemia gene (MLL) and CALM (Clathrin assembly lymphoidmyeloid leukemia gene) are believed to play a role in leukemogenesis.Similarly, disruptions in Rab7 and Rab9, as well as HIP1(Huntingtin-interacting protein), genes that are believed to be involvedin endocytosis, are potentially responsible for ailments resulting inlipid storage, and neuronal diseases, respectively. For these reasons,siRNA directed against clathrin and other genes involved in theclathrin-mediated endocytotic pathway are potentially important researchand therapeutic tools.

siRNAs directed against genes involved in the clathrin-mediatedendocytosis pathways were selected using Formula VIII. The targetedgenes were clathrin heavy chain (CHC, accession #NM_(—)004859), clathrinlight chain A (CLCa, NM_(—)001833), clathrin light chain B (CLCb,NM_(—)001834), CALM (U45976), β2 subunit of AP-2 (β2, NM_(—)001282),Eps15 (NM_(—)001981), Eps15R (NM_(—)021235), dynamin II (DYNII,NM_(—)004945), Rab5a (BC001267), Rab5b (NM_(—)002868), Rab5c (AF141304),and EEA.1 (XM_(—)018197).

For each gene, four siRNAs duplexes with the highest scores wereselected and a BLAST search was conducted for each of them using theHuman EST database. In order to minimize the potential for off-targetsilencing effects, only those sequences with more than three mismatchesagainst un-related sequences were used. All duplexes were synthesized atDharmacon, Inc. as 21-mers with 3′-UU overhangs using a modified methodof 2′-ACE chemistry Scaringe, Advanced 5′-silyl-2′-orthoester approachto RNA oligonucleotide synthesis, Methods Enzymol 2000, 317:3 and theantisense strand was chemically phosphorylated to insure maximizedactivity.

HeLa cells were grown in Dulbecco's modified Eagle's medium (DMEM)containing 10% fetal bovine serum, antibiotics and glutamine. siRNAduplexes were resuspended in 1×siRNA Universal buffer (Dharmacon, Inc.)to 20 μM prior to transfection. HeLa cells in 12-well plates weretransfected twice with 4 μl of 20 μM siRNA duplex in 3 μl Lipofectamine2000 reagent (Invitrogen, Carlsbad, Calif., USA) at 24-hour intervals.For the transfections in which 2 or 3 siRNA duplexes were included, theamount of each duplex was decreased, so that the total amount was thesame as in transfections with single siRNAs. Cells were plated intonormal culture medium 12 hours prior to experiments, and protein levelswere measured 2 or 4 days after the first transfection.

Equal amounts of lysates were resolved by electrophoresis, blotted, andstained with the antibody specific to targeted protein, as well asantibodies specific to unrelated proteins, PP1 phosphatase and Tsg101(not shown). The cells were lysed in Triton X-100/glycerolsolubilization buffer as described previously. Tebar, Bohlander, &Sorkin, Clathrin Assembly Lymphoid Myeloid Leukemia (CALM) Protein:Localization in Endocytic-coated Pits, Interactions with Clathrin, andthe Impact of Overexpression on Clathrin-mediated Traffic, Mol. Biol.Cell August 1999, 10:2687. Cell lysates were electrophoresed,transferred to nitrocellulose membranes, and Western blotting wasperformed with several antibodies followed by detection using enhancedchemiluminescence system (Pierce, Inc). Several x-ray films wereanalyzed to determine the linear range of the chemiluminescence signals,and the quantifications were performed using densitometry andAlphaImager v5.5 software (Alpha Innotech Corporation). In experimentswith Eps15R-targeted siRNAs, cell lysates were subjected toimmunoprecipitation with Ab860, and Eps15R was detected inimmunoprecipitates by Western blotting as described above.

The antibodies to assess the levels of each protein by Western blot wereobtained from the following sources: monoclonal antibody to clathrinheavy chain (TD.1) was obtained from American Type Culture Collection(Rockville, Md., USA); polyclonal antibody to dynamin II was obtainedfrom Affinity Bioreagents, Inc. (Golden, Colo., USA); monoclonalantibodies to EEA.1 and Rab5a were purchased from BD TransductionLaboratories (Los Angeles, Calif., USA); the monoclonal antibody toTsg101 was purchased from Santa Cruz Biotechnology, Inc. (Santa Cruz,Calif., USA); the monoclonal antibody to GFP was from ZYMED LaboratoriesInc. (South San Francisco, Calif., USA); the rabbit polyclonalantibodies Ab32 specific to α-adaptins and Ab20 to CALM were describedpreviously Sorkin, et al, Stoichiometric Interaction of the EpidermalGrowth Factor Receptor with the Clathrin-associated Protein ComplexAP-2, J. Biol. Chem. January 1995, 270:619, the polyclonal antibodies toclathrin light chains A and B were kindly provided by Dr. F. Brodsky(UCSF); monoclonal antibodies to PP1 (BD Transduction Laboratories) andα-Actinin (Chemicon) were kindly provided by Dr. M. Dell'Acqua(University of Colorado); Eps15 Ab577 and Eps15R Ab860 were kindlyprovided by Dr. P. P. Di Fiore (European Cancer Institute).

FIG. 11 demonstrates the in vivo functionality of 48 individual siRNAs,selected using Formula VIII (most of them will meet the criteriaincorporated by Formula IX as well) targeting 12 genes. Various celllines were transfected with siRNA duplexes (Dup1-4) or pools of siRNAduplexes (Pool), and the cells were lysed 3 days after transfection withthe exception of CALM (2 days) and β2 (4 days).

Note a β1-adaptin band (part of AP-1 Golgi adaptor complex) that runsslightly slower than β2 adaptin. CALM has two splice variants, 66 and 72kD. The full-length Eps15R (a doublet of 130 kD) and several truncatedspliced forms of ˜100 kD and ˜70 kD were detected in Eps15Rimmunoprecipitates (shown by arrows). The cells were lysed 3 days aftertransfection. Equal amounts of lysates were resolved by electrophoresisand blotted with the antibody specific to a targeted protein (GFPantibody for YFP fusion proteins) and the antibody specific to unrelatedproteins PP1 phosphatase or α-actinin, and TSG101. The amount of proteinin each specific band was normalized to the amount of non-specificproteins in each lane of the gel. Nearly all of them appear to befunctional, which establishes that Formula VIII and IX can be used topredict siRNAs' functionality in general in a genome wide manner.

To generate the fusion of yellow fluorescent protein (YFP) with Rab5b orRab5c (YFP-Rab5b or YFP-Rab5c), a DNA fragment encoding the full-lengthhuman Rab5b or Rab5c was obtained by PCR using Pft polymerase(Stratagene) with a SacI restriction site introduced into the 5′ end anda KpnI site into the 3′ end and cloned into pEYFP-C1 vector (CLONTECH,Palo Alto, Calif., USA). GFP-CALM and YFP-Rab5a were describedpreviously Tebar, Bohlander, & Sorkin, Clathrin Assembly LymphoidMyeloid Leukemia (CALM) Protein: Localization in Endocytic-coated Pits,Interactions with Clathrin, and the Impact of Overexpression onClathrin-mediated Traffic, Mol. Biol. Cell August 1999, 10:2687.

Example IV Validation of the Algorithm Using Eg5, GADPH, ATE1, MEK2,MEK1, QB, LaminA/C, c-myc, human cyclophilin, and mouse cyclophilin

A number of genes have been identified as playing potentially importantroles in disease etiology. Expression profiles of normal and diseasedkidneys has implicated Edg5 in immunoglobulin A neuropathy, a commonrenal glomerular disease. Myc1, MEK1/2 and other related kinases havebeen associated with one or more cancers, while lamins have beenimplicated in muscular dystrophy and other diseases. For these reasons,siRNA directed against the genes encoding these classes of moleculeswould be important research and therapeutic tools.

FIG. 12 illustrates four siRNAs targeting 10 different genes (Table Vfor sequence and accession number information) that were selectedaccording to the Formula VIII and assayed as individuals and pools inHEK293 cells. The level of siRNA induced silencing was measured usingthe B-DNA assay. These studies demonstrated that thirty-six out of theforty individual SMART-selected siRNA tested are functional (90%) andall 10 pools are fully functional. TABLE V Gene Accession FormulaFormula Name Number SEQ. ID NO. FTllSeqTence VIII IX CLTC NM_004859 SEQ.ID NO. 0427 GAAAGAATCTGTAGAGAAA 76 94.2 CLTC NM_004859 SEQ. ID NO. 0428GCAATGAGCTGTTTGAAGA 65 39.9 CLTC NM_004859 SEQ. ID NO. 0429TGACAAAGGTGGATAAATT 57 38.2 CLTC NM_004859 SEQ. ID NO. 0430GGAAATGGATCTCTTTGAA 54 49.4 CLTA NM_001833 SEQ. ID NO. 0431GGAAAGTAATGGTCCAACA 22 55.5 CLTA NM_001833 SEQ. ID NO. 0432AGACAGTTATGCAGCTATT 4 22.9 CLTA NM_001833 SEQ. ID NO. 0433CCAATTCTCGGAAGCAAGA 1 17 CLTA NM_001833 SEQ. ID NO. 0434GAAAGTAATGGTCCAACAG −1 −13 CLTB NM_001834 SEQ. ID NO. 0435GCGCCAGAGTGAACAAGTA 17 57.5 CLTB NM_001834 SEQ. ID NO. 0436GAAGGTGGCCCAGCTATGT 15 −8.6 CLTB NM_001834 SEQ. ID NO. 0311GGAACCAGCGCCAGAGTGA 13 40.5 CLTB NM_001834 SEQ. ID NO. 0312GAGCGAGATTGCAGGCATA 20 61.7 CALM U45976 SEQ. ID NO. 0313GTTAGTATCTGATGACTTG 36 −34.6 CALM U45976 SEQ. ID NO. 0314GAAATGGAACCACTAAGAA 33 46.1 CALM U45976 SEQ. ID NO. 0315GGAAATGGAACCACTAAGA 30 61.2 CALM U45976 SEQ. ID NO. 0316CAACTACACTTTCCAATGC 28 6.8 EPS15 NM_001981 SEQ. ID NO. 0317CCACCAAGATTTCATGATA 48 25.2 EPS15 NM_001981 SEQ. ID NO. 0318GATCGGAACTCCAACAAGA 43 49.3 EPS15 NM_001981 SEQ. ID NO. 0319AAACGGAGCTACAGATTAT 39 11.5 EPS15 NM_001981 SEQ. ID NO. 0320CCACACAGCATTCTTGTAA 33 −23.6 EPS15R NM_021235 SEQ. ID NO. 0321GAAGTTACCTTGAGCAATC 48 33 EPS15R NM_021235 SEQ. ID NO. 0322GGACTTGGCCGATCCAGAA 27 33 EPS15R NM_021235 SEQ. ID NO. 0323GCACTTGGATCGAGATGAG 20 1.3 EPS15R NM_021235 SEQ. ID NO. 0324CAAAGACCAATTCGCGTTA 17 27.7 DNM2 NM_004945 SEQ. ID NO. 0325CCGAATCAATCGCATCTTC 6 −29.6 DNM2 NM_004945 SEQ. ID NO. 0326GACATGATCCTGCAGTTCA 5 −14 DNM2 NM_004945 SEQ. ID NO. 0327GAGCGAATCGTCACCACTT 5 24 DNM2 NM_004945 SEQ. ID NO. 0328CCTCCGAGCTGGCGTCTAC −4 −63.6 ARF6 AF93885 SEQ. ID NO. 0329TCACATGGTTAACCTCTAA 27 −21.1 ARF6 AF93885 SEQ. ID NO. 0330GATGAGGGACGCCATAATC 7 −38.4 ARF6 AF93885 SEQ. ID NO. 0331CCTCTAACTACAAATCTTA 4 16.9 ARF6 AF93885 SEQ. ID NO. 0332GGAAGGTGCTATCCAAAAT 4 11.5 RAB5A BC001267 SEQ. ID NO. 0333GCAAGCAAGTCCTAACATT 40 25.1 RAB5A BC001267 SEQ. ID NO. 0334GGAAGAGGAGTAGACCTTA 17 50.1 RAB5A BG001267 SEQ. ID NO. 0335AGGAATCAGTGTTGTAGTA 16 11.5 RAB5A BC001267 SEQ. ID NO. 0336GAAGAGGAGTAGACCTTAC 12 7 RAB5B NM_002868 SEQ. ID NO. 0337GAAAGTCAAGCCTGGTATT 14 18.1 RAB5B NM_002868 SEQ. ID NO. 0338AAAGTCAAGCCTGGTATTA 6 −17.8 RAB5B NM_002868 SEQ. ID NO. 0339GCTATGAACGTGAATGATC 3 −21.1 RAB5B NM_002868 SEQ. ID NO. 0340CAAGCCTGGTATTACGTTT −7 −37.5 RAB5C AF141304 SEQ. ID NO. 0341GGAACAAGATCTGTCAATT 38 51.9 RAB5C AF141304 SEQ. ID NO. 0342GCAATGAACGTGAACGAAA 29 43.7 RAB5C AF141304 SEQ. ID NO. 0343CAATGAACGTGAACGAAAT 18 43.3 RAB5C AF141304 SEQ. ID NO. 0344GGACAGGAGCGGTATCACA 6 18.2 EEA1 XM_018197 SEQ. ID NO. 0345AGACAGAGCTTGAGAATAA 67 64.1 EEA1 XM_018197 SEQ. ID NO. 0346GAGAAGATCTTTATGCAAA 60 48.7 EEA1 XM_018197 SEQ. ID NO. 0347GAAGAGAAATCAGCAGATA 58 45.7 EEA1 XM_018197 SEQ. ID NO. 0348GCAAGTAACTCAACTAACA 56 72.3 AP2B1 NM_001282 SEQ. ID NO. 0349GAGCTAATCTGCCACATTG 49 −12.4 AP2B1 NM_001282 SEQ. ID NO. 0350GCAGATGAGTTACTAGAAA 44 48.9 AP2B1 NM_001282 SEQ. ID NO. 0351CAACTTAATTGTCCAGAAA 41 28.2 AP2B1 NM_001282 SEQ. ID NO. 0352CAACACAGGATTCTGATAA 33 −5.8 PLK NM_005030 SEQ. ID NO. 0353AGATTGTGCCTAAGTCTCT −35 −3.4 PLK NM_005030 SEQ. ID NO. 0354ATGAAGATCTGGAGGTGAA 0 −4.3 PLK NM_005030 SEQ. ID NO. 0355TTTGAGACTTCTTGCCTAA −5 −27.7 PLK NM_005030 SEQ. ID NO. 0356AGATCACCCTCCTTAAATA 15 72.3 GAPDH NM_002046 SEQ. ID NO. 0357CAACGGATTTGGTCGTATT 27 −2.8 GAPDH NM_002046 SEQ. ID NO. 0358GAAATCCCATCACCATCTT 24 3.9 GAPDH NM_002046 SEQ. ID NO. 0359GACCTCAACTACATGGTTT 22 −22.9 GAPDH NM_002046 SEQ. ID NO. 0360TGGTTTACATGTTCCAATA 9 9.8 c-Myc SEQ. ID NO. 0361 GAAGAAATCGATGTTGTTT 31−11.7 c-Myc SEQ. ID NO. 0362 ACACAAACTTGAACAGCTA 22 51.3 c-Myc SEQ. IDNO. 0363 GGAAGAAATCGATGTTGTT 18 26 c-Myc SEQ. ID NO. 0364GAAACGACGAGAACAGTTG 18 −8.9 MAP2K1 NM_002755 SEQ. ID NO. 0365GCACATGGATGGAGGTTCT 26 16 MAP2K1 NM_002755 SEQ. ID NO. 0366GCAGAGAGAGCAGATTTGA 16 0.4 MAP2K1 NM_002755 SEQ. ID NO. 0367GAGGTTCTCTGGATCAAGT 14 15.5 MAP2K1 NM_002755 SEQ. ID NO. 0368GAGCAGATTTGAAGCAACT 14 18.5 MAP2K2 NM_030662 SEQ. ID NO. 0369CAAAGACGATGACTTCGAA 37 26.4 MAP2K2 NM_030662 SEQ. ID NO. 0370GATCAGCATTTGCATGGAA 24 −0.7 MAP2K2 NM_030662 SEQ. ID NO. 0371TCCAGGAGTTTGTCAATAA 17 −4.5 MAP2K2 NM_030662 SEQ. ID NO. 0372GGAAGCTGATCCACCTTGA 16 59.2 KNSL1(EG5) NM_004523 SEQ. ID NO. 0373GCAGAAATCTAAGGATATA 53 35.8 KNSL1(EG5) NM_004523 SEQ. ID NO. 0374CAACAAGGATGAAGTCTAT 50 18.3 KNSL1(EG5) NM_004523 SEQ. ID NO. 0375CAGCAGAAATCTAAGGATA 41 32.7 KNSL1(EG5) NM_004523 SEQ. ID NO. 0376CTAGATGGCTTTCTCAGTA 39 3.9 CyclophilinA_(—) NM_021130 SEQ. ID NO. 0377AGACAAGGTCCCAAAGACA −16 58.1 CyclophilinA_(—) NM_021130 SEQ. ID NO. 0378GGAATGGCAAGACCAGCAA −6 36 CyclophilinA_(—) NM_021130 SEQ. ID NO. 0379AGAATTATTCCAGGGTTTA −3 16.1 CyclophilinA_(—) NM_021130 SEQ. ID NO. 0380GCAGACAAGGTCCCAAAGA 8 8.9 LAMIN A/C NM_170707 SEQ. ID NO. 0381AGAAGCAGCTTCAGGATGA 31 38.8 LAMIN A/C NM_170707 SEQ. ID NO. 0382GAGCTTGACTTCCAGAAGA 33 22.4 LAMIN A/C NM_170707 SEQ. ID NO. 0383CCACCGAAGTTCACCCTAA 21 27.5 LAMIN A/C NM_170707 SEQ. ID NO. 0384GAGAAGAGCTCCTCCATCA 55 30.1 CyclophilinB M60857 SEQ. ID NO. 0385GAAAGAGCATCTACGGTGA 41 83.9 CyclophilinB M60857 SEQ. ID NO. 0386GAAAGGATTTGGCTACAAA 53 59.1 CyclophilinB M60857 SEQ. ID NO. 0387ACAGCAAATTCCATCGTGT −20 28.8 CyclophilinB M60857 SEQ. ID NO. 0388GGAAAGACTGTTCCAAAAA 2 27 DBI1 NM_020548 SEQ. ID NO. 0389CAACACGCCTCATCCTCTA 27 −7.6 DBI2 NM_020548 SEQ. ID NO. 0390CATGAAAGCTTACATCAAC 25 −30.8 DBI3 NM_020548 SEQ. ID NO. 0391AAGATGCCATGAAAGCTTA 17 22 DBI4 NM_020548 SEQ. ID NO. 0392GCACATACCGCCTGAGTCT 15 3.9 rLUC1 SEQ. ID NO. 0393 GATCAAATCTGAAGAAGGA 5749.2 rLUC2 SEQ. ID NO. 0394 GCCAAGAAGTTTCCTAATA 50 13.7 rLUC3 SEQ. IDNO. 0395 CAGCATATCTTGAACCATT 41 −2.2 rLUC4 SEQ. ID NO. 0396GAACAAAGGAAACGGATGA 39 29.2 SeAP1 NM_031313 SEQ. ID NO. 0397CGGAAACGGTCCAGGCTAT 6 26.9 SeAP2 NM_031313 SEQ. ID NO. 0398GCTTCGAGCAGACATGATA 4 −11.2 SeAP3 NM_031313 SEQ. ID NO. 0399CCTACACGGTCCTCCTATA 4 4.9 SeAP4 NM_031313 SEQ. ID NO. 0400GCCAAGAACCTCATCATCT 1 −9.9 fLUC1 SEQ. ID NO. 0401 GATATGGGCTGAATACAAA 5440.4 fLUC2 SEQ. ID NO. 0402 GCACTCTGATTGACAAATA 47 54.7 fLUC3 SEQ. IDNO. 0403 TGAAGTCTCTGATTAAGTA 46 34.5 fLUC4 SEQ. ID NO. 0404TCAGAGAGATCCTCATAAA 40 11.4 mCyclo_1 NM_008907 SEQ. ID NO. 0405GCAAGAAGATCACCATTTC 52 46.4 mCyclo_2 NM_008907 SEQ. ID NO. 0406GAGAGAAATTTGAGGATGA 36 70.7 mCyclo_3 NM_008907 SEQ. ID NO. 0407GAAAGGATTTGGCTATAAG 35 −1.5 mCyclo_4 NM_008907 SEQ. ID NO. 0408GAAAGAAGGCATGAACATT 27 10.3 BCL2_1 NM_000633 SEQ. ID NO. 0409GGGAGATAGTGATGAAGTA 21 72 BCL2_2 NM_000633 SEQ. ID NO. 0410GAAGTACATCCATTATAAG 1 3.3 BCL2_3 NM_000633 SEQ. ID NO. 0411GTACGACAACCGGGAGATA 1 35.9 BCL2_4 NM_000633 SEQ. ID NO. 0412AGATAGTGATGAAGTACAT −12 22.1 BCL2_5 NM_000633 SEQ. ID NO. 0413TGAAGACTCTGCTCAGTTT 36 19.1 BCL2_6 NM_000633 SEQ. ID NO. 0414GCATGCGGCCTCTGTTTGA 5 −9.7 QB1 NM_003365.1 SEQ. ID NO. 0415GCACACAGCUUACUACAUC 52 −4.8 QB2 NM_003365.1 SEQ. ID NO. 0416GAAAUGCCCUGGUAUCUCA 49 22.1 QB3 NM_003365.1 SEQ. ID NO. 0417GAAGGAACGUGAUGUGAUC 34 22.9 QB4 NM_003365.1 SEQ. ID NO. 0418GCACUACUCCUGUGUGUGA 28 20.4 ATE1-1 NM_007041 SEQ. ID NO. 0419GAACCCAGCUGGAGAACUU 45 15.5 ATE1-2 NM_007041 SEQ. ID NO. 0420GAUAUACAGUGUGAUCUUA 40 12.2 ATE1-3 NM_007041 SEQ. ID NO. 0421GUACUACGAUCCUGAUUAU 37 32.9 ATE1-4 NM_007041 SEQ. ID NO. 0422GUGCCGACCUUUACAAUUU 35 18.2 EGFR-1 NM_005228 SEQ. ID NO. 0423GAAGGAAACTGAATTCAAA 68 79.4 EGFR-1 NM_005228 SEQ. ID NO. 0424GGAAATATGTACTACGAAA 49 49.5 EGFR-1 NM_005228 SEQ. ID NO. 0425CCACAAAGCAGTGAATTTA 41 7.6 EGFR-1 NM_005228 SEQ. ID NO. 0426GTAACAAGCTCACGCAGTT 40 25.9

Example V Validation of the Algorithm Using Bcl2

Bcl-2 is a ˜25 kD, 205-239 amino acid, anti-apoptotic protein thatcontains considerable homology with other members of the BCL familyincluding BCLX, MCL1, BAX, BAD, and BIK. The protein exists in at leasttwo forms (Bcl2a, which has a hydrophobic tail for membrane anchorage,and Bcl2b, which lacks the hydrophobic tail) and is predominantlylocalized to the mitochondrial membrane. While Bcl2 expression is widelydistributed, particular interest has focused on the expression of thismolecule in B and T cells. Bcl2 expression is down-regulated in normalgerminal center B cells yet in a high percentage of follicularlymphomas, Bcl2 expression has been observed to be elevated. Cytologicalstudies have identified a common translocation ((14;18)(q32;q32))amongst a high percentage (>70%) of these lymphomas. This genetic lesionplaces the Bcl2 gene in juxtaposition to immunoglobulin heavy chain gene(IgH) encoding sequences and is believed to enforce inappropriate levelsof gene expression, and resistance to programmed cell death in thefollicle center B cells. In other cases, hypomethylation of the Bcl2promoter leads to enhanced expression and again, inhibition ofapoptosis. In addition to cancer, dysregulated expression of Bcl-2 hasbeen correlated with multiple sclerosis and various neurologicaldiseases.

The correlation between Bcl-2 translocation and cancer makes this genean attractive target for RNAi. Identification of siRNA directed againstthe bcl2 transcript (or Bcl2-IgH fusions) would further ourunderstanding Bcl2 gene function and possibly provide a futuretherapeutic agent to battle diseases that result from altered expressionor function of this gene.

In Silico Identification of Functional siRNA

To identify functional and hyperfunctional siRNA against the Bcl2 gene,the sequence for Bcl-2 was downloaded from the NCBI Unigene database andanalyzed using the Formula VIII algorithm. As a result of theseprocedures, both the sequence and SMARTscores™ of the Bcl2 siRNA wereobtained and ranked according to their functionality. Subsequently,these sequences were BLAST'ed (database) to insure that the selectedsequences were specific and contained minimal overlap with unrealatedgenes. The SMARTscores™ for the top 10 Bcl-2 siRNA are identified inFIG. 13.

In Vivo Testing of Bcl-2 SiRNA

Bcl-2 siRNAs having the top ten SMARTscores™ were selected and tested ina functional assay to determine silencing efficiency. To accomplishthis, each of the ten duplexes were synthesized using 2′-O-ACE chemistryand transfected at 100 nM concentrations into cells. Twenty-four hourslater assays were performed on cell extracts to assess the degree oftarget silencing. Controls used in these experiments included mocktransfected cells, and cells that were transfected with a non-specificsiRNA duplex.

The results of these experiments are presented below (and in FIG. 14)and show that all ten of the selected siRNA induce 80% or bettersilencing of the Bcl2 message at 100 nM concentrations. These dataverify that the algorithm successfully identified functional Bcl2 siRNAand provide a set of functional agents that can be used in experimentaland therapeutic environments. siRNA 1 GGGAGAUAGUGAUGAAGUA SEQ. ID NO.301 siRNA 2 GAAGUACAUCCAUUAUAAG SEQ. ID NO. 302 siRNA 3GUACGACAACCGGGAGAUA SEQ. ID NO. 303 siRNA 4 AGAUAGUGAUGAAGUACAU SEQ. IDNO. 304 siRNA 5 UGAAGACUCUGCUCAGUUU SEQ. ID NO. 305 siRNA 6GCAUGCGGCCUCUGUUUGA SEQ. ID NO. 306 siRNA 7 UGCGGCCUCUGUUUGAUUU SEQ. IDNO. 307 siRNA 8 GAGAUAGUGAUGAAGUACA SEQ. ID NO. 308 siRNA 9GGAGAUAGUGAUGAAGUAC SEQ. ID NO. 309 siRNA 10 GAAGACUCUGCUCAGUUUG SEQ. IDNO. 310 Bcl2 siRNA: Sense Strand, 5′→3′

Example VI Evidence for the Benefits of Pooling

Evidence for the benefits of pooling have been demonstrated using thereporter gene, luciferase. Ninety siRNA duplexes were synthesized usingDharmacon proprietary ACE® chemistry against one of the standardreporter genes: firefly luciferase. The duplexes were designed to starttwo base pairs apart and to cover approximately 180 base pairs of theluciferase gene (see sequences in Table III). Subsequently, the siRNAduplexes were co-transfected with a luciferase expression reporterplasmid into HEK293 cells using standard transfection protocols andluciferase activity was assayed at 24 and 48 hours.

Transfection of individual siRNAs showed standard distribution ofinhibitory effect. Some duplexes were active, while others were not.FIG. 15 represents a typical screen of ninety siRNA duplexes (SEQ. IDNO. 0032-0120) positioned two base pairs apart. As the figure suggests,the functionality of the siRNA duplex is determined more by a particularsequence of the oligonucleotide than by the relative oligonucleotideposition within a gene or excessively sensitive part of the mRNA, whichis important for traditional anti-sense technology.

When two continuous oligonucleotides were pooled together, a significantincrease in gene silencing activity was observed (see FIGS. 16A and16B). A gradual increase in efficacy and the frequency of poolsfunctionality was observed when the number of siRNAs increased to 3 and4 (see FIGS. 16A, 16B, 17A, and 17B). Further, the relative positioningof the oligonucleotides within a pool did not determine whether aparticular pool was functional (see FIGS. 18A and 18B, in which 100% ofpools of oligonucleotides distanced by 2, 10 and 20 base pairs werefunctional).

However, relative positioning may nonetheless have an impact. Anincreased functionality may exist when the siRNA are positionedcontinuously head to toe (5′ end of one directly adjacent to the 3′ endof the others).

Additionally, siRNA pools that were tested performed at least as well asthe best oligonucleotide in the pool, under the experimental conditionswhose results are depicted in FIG. 19. Moreover, when previouslyidentified non-functional and marginally (semi) functional siRNAduplexes were pooled together in groups of five at a time, a significantfunctional cooperative action was observed. (See FIG. 20) In fact, poolsof semi-active oligonucleotides were 5 to 25 times more functional thanthe most potent oligonucleotide in the pool. Therefore, pooling severalsiRNA duplexes together does not interfere with the functionality of themost potent siRNAs within a pool, and pooling provides an unexpectedsignificant increase in overall functionality

Example VII Pooling Across Species

Experiments were performed on the following genes: β-galactosidase,Renilla luciferase, and Secreted alkaline phosphatase, which demonstratethe benefits of pooling. (see FIG. 21) Approximately 50% of individualsiRNAs designed to silence the above-specified genes were functional,while 100% of the pools that contain the same siRNA duplexes werefunctional.

Example VIII Highly Functional siRNA

Pools of five siRNAs in which each two siRNAs overlap to 10-90% resultedin 98% functional entities (>80% silencing). Pools of siRNAs distributedthroughout the mRNA that were evenly spaced, covering an approximate20-2000 base pair range, were also functional. When the pools of siRNAwere positioned continuously head to tail relative to mRNA sequences andmimicked the natural products of Dicer cleaved long double stranded RNA,98% of the pools evidenced highly functional activity (>95% silencing).

Example IX Human Cyclophyline

Table III above lists the siRNA sequences for the human cyclophylineprotein. A particularly functional siRNA may be selected by applyingthese sequences to any of Formula I to VII above.

Alternatively, one could pool 2, 3, 4, 5 or more of these sequences tocreate a kit for silencing a gene. Preferably, within the kit therewould be at least one sequence that has a relatively high predictedfunctionality when any of Formulas I-VII is applied.

Example X Validation of Multigene Knockout Using Rab5 and Eps

Two or more genes having similar, overlapping functions often leads togenetic redundancy. Mutations that knockout only one of, e.g., a pair ofsuch genes (also referred to as homologs) result in little or nophenotype due to the fact that the remaining intact gene is capable offulfilling the role of the disrupted counterpart. To fully understandthe function of such genes in cellular physiology, it is often necessaryto knockout or knockdown both homologs simultaneously. Unfortunately,concomitant knockdown of two or more genes is frequently difficult toachieve in higher organisms (e.g. mice) thus it is necessary tointroduce new technologies dissect gene function. One such approach toknocking down multiple genes simultaneously is by using siRNA. Forexample, FIG. 11 showed that rationally designed siRNA directed againsta number of genes involved in the clathrin-mediated endocytosis pathwayresulted in significant levels of protein reduction (e.g. >80%). Todetermine the effects of gene knockdown on clathrin-related endocytosis,internalization assays were performed using epidermal growth factor andtransferrin. Specifically, mouse receptor-grade EGF (CollaborativeResearch Inc.) and iron-saturated human transferrin (Sigma) wereiodinated as described previously (Jiang, X., Huang, F., Marusyk, A. &Sorkin, A. (2003) Mol Biol Cell 14, 858-70). HeLa cells grown in 12-welldishes were incubated with ¹²⁵I-EGF (1 ng/ml) or ¹²⁵I-transferrin (1μg/ml) in binding medium (DMEM, 0.1% bovine serum albumin) at 37° C.,and the ratio of internalized and surface radioactivity was determinedduring 5-min time course to calculate specific internalization rateconstant k_(e) as described previously (Jiang, X et al.). Themeasurements of the uptakes of radiolabeled transferrin and EGF wereperformed using short time-course assays to avoid influence of therecycling on the uptake kinetics, and using low ligand concentration toavoid saturation of the clathrin-dependent pathway (for EGF Lund, K. A.,Opresko, L. K., Strarbuck, C., Walsh, B. J. & Wiley, H. S. (1990) J.Biol. Chem. 265, 15713-13723).

The effects of knocking down Rab5a, 5b, 5c, Eps, or Eps15R(individually) are shown in FIG. 22 and demonstrate that disruption ofsingle genes has little or no effect on EGF or Tfn internalization. Incontrast, simultaneous knock down of Rab5a, 5b, and 5c, or Eps andEps15R, leads to a distinct phenotype (note: total concentration ofsiRNA in these experiments remained constant with that in experiments inwhich a single siRNA was introduced, see FIG. 23). These experimentsdemonstrate the effectiveness of using rationally designed siRNA toknockdown multiple genes and validates the utility of these reagents tooverride genetic redundancy.

Example XI Validation of Multigene Targeting Using G6PD, GAPDH, PLK, andUQC

Further demonstration of the ability to knock down expression ofmultiple genes using rationally designed siRNA was performed using poolsof siRNA directed against four separate genes. To achieve this, siRNAwere transfected into cells (total siRNA concentration of 100 nM) andassayed twenty-four hours later by B-DNA. Results shown in FIG. 24 showthat pools of rationally designed molecules are capable ofsimultaneously silencing four different genes.

Example XII Identifying Hyperfunctional siRNA

Identification of Hyperfunctional Bcl-2 siRNA

The ten rationally designed Bcl2 siRNA (identified in FIGS. 13, 14) weretested to identify hyperpotent reagents. To accomplish this, each of theten Bcl-2 siRNA were individually transfected into cells at a 300 pM(0.3 nM) concentrations. Twenty-four hours later, transcript levels wereassessed by B-DNA assays and compared with relevant controls. As shownin FIG. 25, while the majority of Bcl-2 siRNA failed to inducefunctional levels of silencing at this concentration, siRNA 1 and 8induced >80% silencing, and siRNA 6 exhibited greater than 90% silencingat this subnanomolar concentration.

Example XIII Gene Silencing Prophetic Example

Below is an example of how one might transfect a cell.

-   -   a. Select a cell line. The selection of a cell line is usually        determined by the desired application. The most important        feature to RNAi is the level of expression of the gene of        interest. It is highly recommended to use cell lines for which        siRNA transfection conditions have been specified and validated.    -   b. Plate the cells. Approximately 24 hours prior to        transfection, plate the cells at the appropriate density so that        they will be approximately 70-90% confluent, or approximately        1×10⁵ cells/ml at the time of transfection. Cell densities that        are too low may lead to toxicity due to excess exposure and        uptake of transfection reagent-siRNA complexes. Cell densities        that are too high may lead to low transfection efficiencies and        little or no silencing. Incubate the cells overnight. Standard        incubation conditions for mammalian cells are 37° C. in 5% CO₂.        Other cell types, such as insect cells, require different        temperatures and CO₂ concentrations that are readily        ascertainable by persons skilled in the art. Use conditions        appropriate for the cell type of interest.    -   c. SiRNA re-suspension. Add 20 μl siRNA universal buffer to each        siRNA to generate a final concentration of 50 μM.

d. SiRNA-lipid complex formation. Use RNase-free solutions and tubes.Using the following table, Table VI: TABLE VI 96-well 24-well Mixture 1(TransIT-TKO-Plasmid dilution mixture) Opti-MEM 9.3 μl 46.5 μlTransIT-TKO (1 μg/μl) 0.5 μl 2.5 μl Mixture 1 Final Volume 10.0 μl 50.0μl Mixture 2 (siRNA dilution mixture) Opti-MEM 9.0 μl 45.0 μl siRNA (1μM) 1.0 μl 5.0 μl Mixture 2 Final Volume 10.0 μl 50.0 μl Mixture 3(siRNA-Transfection reagent mixture) Mixture 1 10 μl 50 μl Mixture 2 10μl 50 μl Mixture 3 Final Volume 20 μl 100 μl Incubate 20 minutes at roomtemperature. Mixture 4 (Media-siRNA/Transfection reagent mixture)Mixture 3 20 μl 100 μl Complete media 80 μl 400 μl Mixture 4 FinalVolume 100 μl 500 μl Incubate 48 hours at 37° C.Transfection. Create a Mixture 1 by combining the specified amounts ofOPTI-MEM serum free media and transfection reagent in a sterilepolystyrene tube. Create a Mixture 2 by combining specified amounts ofeach siRNA with OPTI-MEM media in sterile 1 ml tubes. Create a Mixture 3by combining specified amounts of Mixture 1 and Mixture 2. Mix gently(do not vortex) and incubate at room temperature for 20 minutes. Createa Mixture 4 by combining specified amounts of Mixture 3 to completemedia. Add appropriate volume to each cell culture well. Incubate cellswith transfection reagent mixture for 24-72 hours at 37° C. Thisincubation time is flexible. The ratio of silencing will remainconsistent at any point in the time period. Assay for gene silencingusing an appropriate detection method such as RT-PCR, Western blotanalysis, immunohistochemistry, phenotypic analysis, mass spectrometry,fluorescence, radioactive decay, or any other method that is now knownor that comes to be known to persons skilled in the art and that fromreading this disclosure would useful with the present invention. Theoptimal window for observing a knockdown phenotype is related to themRNA turnover of the gene of interest, although 24-72 hours is standard.Final Volume reflects amount needed in each well for the desired cellculture format. When adjusting volumes for a Stock Mix, an additional10% should be used to accommodate variability in pipetting, etc.Duplicate or triplicate assays should be carried out when possible.

While the invention has been described in connection with specificembodiments thereof, it will be understood that it is capable of furthermodifications and this application is intended to cover any variations,uses, or adaptations of the invention following, in general, theprinciples of the invention and including such departure from thepresent disclosure as come within known or customary practice within theart to which the invention pertains and as may be applied to theessential features hereinbefore set forth and as follows in the scope ofthe appended claims.

1. An siRNA molecule effective at silencing Bcl-2 expression, whereinsaid molecule comprises a sense region and an antisense region, whereinsaid sense region and said antisense region together form a duplexregion comprising 19-30 base pairs and said antisense region comprises asequence that is the reverse complement of SEQ. ID NO.
 303. 2. The siRNAof claim 1, wherein said antisense region and said sense region are each19-25 nucleotides in length.
 3. The siRNA of claim 1, wherein saidantisense region and said sense region are each 19 nucleotides inlength.
 4. The siRNA of claim 1, wherein said antisense region is 100%complementary to a target Bcl-2 RNA.
 5. The siRNA molecule of claim 1,wherein said siRNA molecule comprises at least one overhang region,wherein said overhang region comprises six or fewer nucleotides.
 6. ThesiRNA molecule of claim 1 wherein said siRNA molecule comprises nooverhang regions.
 7. The siRNA molecule of claim 2 wherein said siRNAmolecule comprises at least one overhang region, wherein said overhangregion comprises six or fewer nucleotides.
 8. The siRNA molecule ofclaim 2 wherein said siRNA molecule comprises no overhang regions. 9.The siRNA molecule of claim 3 wherein said siRNA molecule comprises atleast one overhang region, wherein said overhang region comprises six orfewer nucleotides.
 10. The siRNA molecule of claim 3 wherein said siRNAmolecule comprises no overhang regions.